Methods for diagnosing irritable bowel syndrome

ABSTRACT

The present invention provides methods, systems, and code for accurately classifying whether a sample from an individual is associated with irritable bowel syndrome (IBS). In particular, the present invention is useful for classifying a sample from an individual as an IBS sample using a statistical algorithm and/or empirical data. The present invention is also useful for ruling out one or more diseases or disorders that present with IBS-like symptoms and ruling in IBS using a combination of statistical algorithms and/or empirical data. Thus, the present invention provides an accurate diagnostic prediction of IBS and prognostic information useful for guiding treatment decisions.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority to U.S. Provisional ApplicationNos. 60/822,488, filed Aug. 15, 2006, 60/884,397, filed Jan. 10, 2007,and 60/895,962, filed Mar. 20, 2007, the disclosures of which are herebyincorporated by reference in their entireties for all purposes.

BACKGROUND OF THE INVENTION

Irritable bowel syndrome (IBS) is the most common of allgastrointestinal disorders, affecting 10-20% of the general populationand accounting for more than 50% of all patients with digestivecomplaints. However, studies suggest that only about 10% to 50% of thoseafflicted with IBS actually seek medical attention. Patients with IBSpresent with disparate symptoms such as, for example, abdominal painpredominantly related to defecation, diarrhea, constipation oralternating diarrhea and constipation, abdominal distention, gas, andexcessive mucus in the stool. More than 40% of IBS patients havesymptoms so severe that they have to take time off from work, curtailtheir social life, avoid sexual intercourse, cancel appointments, stoptraveling, take medication, and even stay confined to their house forfear of embarrassment. The estimated health care cost of IBS in theUnited States is $8 billion per year (Talley et al., Gastroenterol.,109:1736-1741 (1995)).

The precise pathophysiology of IBS is not well understood. Nevertheless,there is a heightened sensitivity to visceral pain perception, known asperipheral sensitization. This sensitization involves a reduction in thethreshold and an increase in the gain of the transduction processes ofprimary afferent neurons, attributable to a variety of mediatorsincluding monoamines (e.g., catecholamines and indoleamines), substanceP, and a variety of cytokines and prostanoids such as E-typeprostaglandins (see, e.g., Mayer et al., Gastroenterol., 107:271-293(1994)). Also implicated in the etiopathology of IBS is intestinal motordysfunction, which leads to abnormal handling of intraluminal contentsand/or gas (see, e.g., Kellow et al., Gastroenterol., 92:1885-1893(1987); Levitt et al., Ann. Int. Med., 124:422-424 (1996)).Psychological factors may also contribute to IBS symptoms appearing inconjunction with, if not triggered by, disturbances including depressionand anxiety (see, e.g., Drossman et al., Gastroenterol. Int., 8:47-90(1995)).

The causes of IBS are not well understood. The walls of the intestinesare lined with layers of muscle that contract and relax as they movefood from the stomach through the intestinal tract to the rectum.Normally, these muscles contract and relax in a coordinated rhythm. InIBS patients, these contractions are typically stronger and last longerthan normal. As a result, food is forced through the intestines morequickly in some cases causing gas, bloating, and diarrhea. In othercases, the opposite occurs: food passage slows and stools become hardand dry causing constipation.

The precise pathophysiology of IBS remains to be elucidated. While gutdysmotility and altered visceral perception are considered importantcontributors to symptom pathogenesis (Quigley, Scand. J. Gastroenterol.,38 (Suppl. 237):1-8 (2003); Mayer et al., Gastroenterol., 122:2032-2048(2002)), this condition is now generally viewed as a disorder of thebrain-gut axis. Recently, roles for enteric infection and intestinalinflammation have also been proposed. Studies have documented the onsetof IBS following bacteriologically confirmed gastroenteritis, whileothers have provided evidence of low-grade mucosal inflammation (Spilleret al., Gut, 47:804-811 (2000); Dunlop et al., Gastroenterol.,125:1651-1659 (2003); Cumberland et al., Epidemiol. Infect., 130:453-460(2003)) and immune activation (Gwee et al., Gut, 52:523-526 (2003);Pimentel et al., Am. J Gastroenterol., 95:3503-3506 (2000)) in IBS. Theenteric flora has also been implicated, and a recent study demonstratedthe efficacy of the probiotic organism Bifidobacterium in treating thedisorder through modulation of immune activity (O'Mahony et al.,Gastroenterol., 128:541-551 (2005)).

The hypothalamic-pituitary-adrenal axis (HPA) is the core endocrinestress system in humans (De Wied et al., Front. Neuroendocrinol.,14:251-302 (1993)) and provides an important link between the brain andthe gut immune system. Activation of the axis takes place in response toboth physical and psychological stressors (Dinan, Br. J. Psychiatry,164:365-371 (1994)), both of which have been implicated in thepathophysiology of IBS (Cumberland et al., Epidemiol. Infect.,130:453-460 (2003)). Patients with IBS have been reported as having anincreased rate of sexual and physical abuse in childhood together withhigher rates of stressful life events in adulthood (Gaynes et al.,Baillieres Clin. Gastroenterol., 13:437-452 (1999)). Such psychosocialtrauma or poor cognitive coping strategy profoundly affects symptomseverity, daily functioning, and health outcome.

Although the etiology of IBS is not fully characterized, the medicalcommunity has developed a consensus definition and criteria, known asthe Rome II criteria, to aid in the diagnosis of IBS based upon patienthistory. The Rome II criteria requires three months of continuous orrecurrent abdominal pain or discomfort over a one-year period that isrelieved by defecation and/or associated with a change in stoolfrequency or consistency as well as two or more of the following:altered stool frequency, altered stool form, altered stool passage,passage of mucus, or bloating and abdominal distention. The absence ofany structural or biochemical disorders that could be causing thesymptoms is also a necessary condition. As a result, the Rome IIcriteria can be used only when there is a substantial patient historyand is reliable only when there is no abnormal intestinal anatomy ormetabolic process that would otherwise explain the symptoms. Similarly,the Rome III criteria recently developed by the medical community can beused only when there is presentation of a specific set of symptoms, adetailed patient history, and a physical examination.

It is well documented that diagnosing a patient as having IBS can bechallenging due to the similarity in symptoms between IBS and otherdiseases or disorders. In fact, because the symptoms of IBS are similaror identical to the symptoms of so many other intestinal illnesses, itcan take years before a correct diagnosis is made. For example, patientswho have inflammatory bowel disease (IBD), but who exhibit mild signsand symptoms such as bloating, diarrhea, constipation, and abdominalpain, may be difficult to distinguish from patients with IBS. As aresult, the similarity in symptoms between IBS and IBD renders rapid andaccurate diagnosis difficult. The difficulty in differentiallydiagnosing IBS and IBD hampers early and effective treatment of thesediseases. Unfortunately, rapid and accurate diagnostic methods fordefinitively distinguishing IBS from other intestinal diseases ordisorders presenting with similar symptoms are currently not available.The present invention satisfies this need and provides relatedadvantages as well.

SUMMARY OF THE INVENTION

The present invention provides methods, systems, and code for accuratelyclassifying whether a sample from an individual is associated withirritable bowel syndrome (IBS). As a non-limiting example, the presentinvention is useful for classifying a sample from an individual as anIBS sample using a statistical algorithm and/or empirical data. Thepresent invention is also useful for ruling out one or more diseases ordisorders that present with IBS-like symptoms and ruling in IBS using acombination of statistical algorithms and/or empirical data. Thus, thepresent invention provides an accurate diagnostic prediction of IBS andprognostic information useful for guiding treatment decisions.

In one aspect, the present invention provides a method for classifyingwhether a sample from an individual is associated with IBS, the methodcomprising:

-   -   (a) determining a diagnostic marker profile by detecting the        presence or level of at least one diagnostic marker in the        sample; and    -   (b) classifying the sample as an IBS sample or non-IBS sample        using an algorithm based upon the diagnostic marker profile.

In some embodiments, the diagnostic marker profile is determined bydetecting the presence or level of at least one diagnostic markerselected from the group consisting of a cytokine, growth factor,anti-neutrophil antibody, anti-Saccharomyces cerevisiae antibody (ASCA),antimicrobial antibody, lactoferrin, anti-tissue transglutaminase (tTG)antibody, lipocalin, matrix metalloproteinase (MMP), tissue inhibitor ofmetalloproteinase (TIMP), alpha-globulin, actin-severing protein, S100protein, fibrinopeptide, calcitonin gene-related peptide (CGRP),tachykinin, ghrelin, neurotensin, corticotropin-releasing hormone, andcombinations thereof.

In a preferred aspect, the present invention provides a method forclassifying whether a sample from an individual is associated with IBS,the method comprising:

-   -   (a) determining a diagnostic marker profile by detecting the        presence or level of at least one diagnostic marker selected        from the group consisting of a cytokine, growth factor,        anti-neutrophil antibody, ASCA, antimicrobial antibody,        lactoferrin, anti-tTG antibody, lipocalin, MMP, TIMP,        alpha-globulin, actin-severing protein, S100 protein,        fibrinopeptide, CGRP, tachykinin, ghrelin, neurotensin,        corticotropin-releasing hormone, and combinations thereofin the        sample; and    -   (b) classifying the sample as an IBS sample or non-IBS sample        using an algorithm based upon the diagnostic marker profile.

In preferred embodiments, the presence or level of 1, 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, ormore of the biomarkers shown in Table 1 is detected to generate adiagnostic marker profile that is useful for predicting IBS. In certaininstances, the biomarkers described herein are analyzed using animmunoassay such as an enzyme-linked immunosorbent assay (ELISA) or animmunohistochemical assay. TABLE 1 Exemplary diagnostic markers suitablefor use in IBS classification. Family Biomarker Cytokine CXCL8/IL-8IL-1β TNF-related weak inducer of apoptosis (TWEAK) LeptinOsteoprotegerin (OPG) CCL19/MIP-3β CXCL1/GRO1/GROα CXCL4/PF-4CXCL7/NAP-2 Growth Factor Epidermal growth factor (EGF) Vascularendothelial growth factor (VEGF) Pigment epithelium-derived factor(PEDF) Brain-derived neurotrophic factor (BDNF) Schwannoma-derivedgrowth factor (SDGF)/ amphiregulin Anti-neutro- Anti-neutrophilcytoplasmic antibody (ANCA) phil antibody Perinuclear anti-neutrophilcytoplasmic antibody (pANCA) ASCA ASCA-IgA ASCA-IgG AntimicrobialAnti-outer membrane protein C (OmpC) antibody antibody Anti-Cbir-1flagellin antibody Lipocalin Neutrophil gelatinase-associated lipocalin(NGAL) MMP MMP-9 TIMP TIMP-1 Alpha- Alpha-2-macroglobulin (α2-MG)globulin Haptoglobin precursor alpha-2 (Hpα2) Orosomucoid Actin-severingGelsolin protein S100 protein Calgranulin A/S100A8/MRP-8 FibrinopeptideFibrinopeptide A (FIBA) Others Lactoferrin Anti-tissue transglutaminase(tTG) antibody Calcitonin gene-related peptide (CGRP)

In other embodiments, the method of ruling in IBS comprises determininga diagnostic marker profile optionally in combination with a symptomprofile, wherein the symptom profile is determined by identifying thepresence or severity of at least one symptom in the individual; andclassifying the sample as an IBS sample or non-IBS sample using analgorithm based upon the diagnostic marker profile and the symptomprofile.

The symptom profile is typically determined by identifying the presenceor severity of at least one symptom selected from the group consistingof chest pain, chest discomfort, heartburn, uncomfortable fullness afterhaving a regular-sized meal, inability to finish a regular-sized meal,abdominal pain, abdominal discomfort, constipation, diarrhea, bloating,abdominal distension, negative thoughts or feelings associated withhaving pain or discomfort, and combinations thereof.

In preferred embodiments, the presence or severity of 1, 2, 3, 4, 5, 6,7, 8, 9, 10, or more of the symptoms described herein is identified togenerate a symptom profile that is useful for predicting IBS. In certaininstances, a questionnaire or other form of written, verbal, ortelephone survey is used to produce the symptom profile. Thequestionnaire or survey typically comprises a standardized set ofquestions and answers for the purpose of gathering information fromrespondents regarding their current and/or recent IBS-related symptoms.

In some embodiments, the symptom profile is produced by compiling and/oranalyzing all or a subset of the answers to the questions set forth inthe questionnaire or survey. In other embodiments, the symptom profileis produced based upon the individual's response to the followingquestion: “Are you currently experiencing any symptoms?” The symptomprofile generated in accordance with either of these embodiments can beused in combination with a diagnostic marker profile in thealgorithmic-based methods described herein to improve the accuracy ofpredicting IBS.

In another aspect, the present invention provides a method forclassifying whether a sample from an individual is associated with IBS,the method comprising:

-   -   (a) determining a diagnostic marker profile by detecting the        presence or level of at least one diagnostic marker in the        sample;    -   (b) classifying the sample as an IBD sample or non-IBD sample        using a first statistical algorithm based upon the diagnostic        marker profile; and    -   if the sample is classified as a non-IBD sample,    -   (c) classifying the non-IBD sample as an IBS sample or non-IBS        sample using a second statistical algorithm based upon the same        diagnostic marker profile as determined in step (a) or a        different diagnostic marker profile.

In some embodiments, the diagnostic marker profile is determined bydetecting the presence or level of at least one diagnostic markerselected from the group consisting of a cytokine, growth factor,anti-neutrophil antibody, ASCA, antimicrobial antibody, lactoferrin,anti-tTG antibody, lipocalin, MMP, TIMP, alpha-globulin, actin-severingprotein, S100 protein, fibrinopeptide, CGRP, tachykinin, ghrelin,neurotensin, corticotropin-releasing hormone, and combinations thereof.

In other embodiments, the method of first ruling out IBD and then rulingin IBS comprises determining a diagnostic marker profile in combinationwith a symptom profile, wherein the symptom profile is determined byidentifying the presence or severity of at least one symptom in theindividual; classifying the sample as an IBD sample or non-IBD sampleusing a first statistical algorithm based upon the diagnostic markerprofile and the symptom profile; and if the sample is classified as anon-IBD sample, classifying the non-IBD sample as an IBS sample ornon-IBS sample using a second statistical algorithm based upon the sameprofiles as determined in step (a) or different profiles.

In yet another aspect, the present invention provides a method formonitoring the progression or regression of IBS in an individual, themethod comprising:

-   -   (a) determining a diagnostic marker profile by detecting the        presence or level of at least one diagnostic marker in the        sample; and    -   (b) determining the presence or severity of IBS in the        individual using an algorithm based upon the diagnostic marker        profile.

In some embodiments, the diagnostic marker profile is determined bydetecting the presence or level of at least one diagnostic markerselected from the group consisting of a cytokine, growth factor,anti-neutrophil antibody, ASCA, antimicrobial antibody, lactoferrin,anti-tTG antibody, lipocalin, MMP, TIMP, alpha-globulin, actin-severingprotein, S100 protein, fibrinopeptide, CGRP, tachykinin, ghrelin,neurotensin, corticotropin-releasing hormone, and combinations thereof.

In other embodiments, the method of monitoring the progression orregression of IBS comprises determining a diagnostic marker profileoptionally in combination with a symptom profile, wherein the symptomprofile is determined by identifying the presence or severity of atleast one symptom in the individual; and determining the presence orseverity of IBS in the individual using an algorithm based upon thediagnostic marker profile and the symptom profile.

In a related aspect, the present invention provides a method formonitoring drug efficacy in an individual receiving a drug useful fortreating IBS, the method comprising:

-   -   (a) determining a diagnostic marker profile by detecting the        presence or level of at least one diagnostic marker in the        sample; and    -   (b) determining the effectiveness of the drug using an algorithm        based upon the diagnostic marker profile.

In some embodiments, the diagnostic marker profile is determined bydetecting the presence or level of at least one diagnostic markerselected from the group consisting of a cytokine, growth factor,anti-neutrophil antibody, ASCA, antimicrobial antibody, lactoferrin,anti-tTG antibody, lipocalin, MMP, TIMP, alpha-globulin, actin-severingprotein, S100 protein, fibrinopeptide, CGRP, tachykinin, ghrelin,neurotensin, corticotropin-releasing hormone, and combinations thereof.

In other embodiments, the method of monitoring IBS drug efficacycomprises determining a diagnostic marker profile optionally incombination with a symptom profile, wherein the symptom profile isdetermined by identifying the presence or severity of at least onesymptom in the individual; and determining the effectiveness of the drugusing an algorithm based upon the diagnostic marker profile and thesymptom profile.

In a further aspect, the present invention provides a computer-readablemedium including code for controlling one or more processors to classifywhether a sample from an individual is associated with IBS, the codecomprising:

instructions to apply a statistical process to a data set comprising adiagnostic marker profile to produce a statistically derived decisionclassifying the sample as an IBS sample or non-IBS sample based upon thediagnostic marker profile,

wherein the diagnostic marker profile indicates the presence or level ofat least one diagnostic marker in the sample.

In some embodiments, the diagnostic marker profile indicates thepresence or level of at least one diagnostic marker selected from thegroup consisting of a cytokine, growth factor, anti-neutrophil antibody,ASCA, antimicrobial antibody, lactoferrin, anti-tTG antibody, lipocalin,MMP, TIMP, alpha-globulin, actin-severing protein, S100 protein,fibrinopeptide, CGRP, tachykinin, ghrelin, neurotensin,corticotropin-releasing hormone, and combinations thereof.

In other embodiments, the computer-readable medium for ruling in IBScomprises instructions to apply a statistical process to a data setcomprising a diagnostic marker profile optionally in combination with asymptom profile which indicates the presence or severity of at least onesymptom in the individual to produce a statistically derived decisionclassifying the sample as an IBS sample or non-IBS sample based upon thediagnostic marker profile and the symptom profile.

In a related aspect, the present invention provides a computer-readablemedium including code for controlling one or more processors to classifywhether a sample from an individual is associated with IBS, the codecomprising:

-   -   (a) instructions to apply a first statistical process to a data        set comprising a diagnostic marker profile to produce a        statistically derived decision classifying the sample as an IBD        sample or non-IBD sample based upon the diagnostic marker        profile, wherein the diagnostic marker profile indicates the        presence or level of at least one diagnostic marker in the        sample; and    -   if the sample is classified as a non-IBD sample,    -   (b) instructions to apply a second statistical process to the        same or different data set to produce a second statistically        derived decision classifying the non-IBD sample as an IBS sample        or non-IBS sample.

In some embodiments, the diagnostic marker profile indicates thepresence or level of at least one diagnostic marker selected from thegroup consisting of a cytokine, growth factor, anti-neutrophil antibody,ASCA, antimicrobial antibody, lactoferrin, anti-tTG antibody, lipocalin,MMP, TIMP, alpha-globulin, actin-severing protein, S100 protein,fibrinopeptide, CGRP, tachykinin, ghrelin, neurotensin,corticotropin-releasing hormone, and combinations thereof.

In other embodiments, the computer-readable medium for first ruling outIBD and then ruling in IBS comprises instructions to apply a firststatistical process to a data set comprising a diagnostic marker profileoptionally in combination with a symptom profile which indicates thepresence or severity of at least one symptom in the individual toproduce a statistically derived decision classifying the sample as anIBD sample or non-IBD sample based upon the diagnostic marker profileand the symptom profile; and if the sample is classified as a non-IBDsample, instructions to apply a second statistical process to the sameor different data set to produce a second statistically derived decisionclassifying the non-IBD sample as an IBS sample or non-IBS sample.

In an additional aspect, the present invention provides a system forclassifying whether a sample from an individual is associated with IBS,the system comprising:

-   -   (a) a data acquisition module configured to produce a data set        comprising a diagnostic marker profile, wherein the diagnostic        marker profile indicates the presence or level of at least one        diagnostic marker in the sample;    -   (b) a data processing module configured to process the data set        by applying a statistical process to the data set to produce a        statistically derived decision classifying the sample as an IBS        sample or non-IBS sample based upon the diagnostic marker        profile; and    -   (c) a display module configured to display the statistically        derived decision.

In some embodiments, the diagnostic marker profile indicates thepresence or level of at least one diagnostic marker selected from thegroup consisting of a cytokine, growth factor, anti-neutrophil antibody,ASCA, antimicrobial antibody, lactoferrin, anti-tTG antibody, lipocalin,MMP, TIMP, alpha-globulin, actin-severing protein, S100 protein,fibrinopeptide, CGRP, tachykinin, ghrelin, neurotensin,corticotropin-releasing hormone, and combinations thereof.

In other embodiments, the system for ruling in IBS comprises a dataacquisition module configured to produce a data set comprising adiagnostic marker profile optionally in combination with a symptomprofile which indicates the presence or severity of at least one symptomin the individual; a data processing module configured to process thedata set by applying a statistical process to the data set to produce astatistically derived decision classifying the sample as an IBS sampleor non-IBS sample based upon the diagnostic marker profile and thesymptom profile; and a display module configured to display thestatistically derived decision.

In a related aspect, the present invention provides a system forclassifying whether a sample from an individual is associated with IBS,the system comprising:

-   -   (a) a data acquisition module configured to produce a data set        comprising a diagnostic marker profile, wherein the diagnostic        marker profile indicates the presence or level of at least one        diagnostic marker in the sample;    -   (b) a data processing module configured to process the data set        by applying a first statistical process to the data set to        produce a first statistically derived decision classifying the        sample as an IBD sample or non-IBD sample based upon the        diagnostic marker profile;    -   if the sample is classified as a non-IBD sample, a data        processing module configured to apply a second statistical        process to the same or different data set to produce a second        statistically derived decision classifying the non-IBD sample as        an IBS sample or non-IBS sample; and    -   (c) a display module configured to display the first and/or the        second statistically derived decision.

In some embodiments, the diagnostic marker profile indicates thepresence or level of at least one diagnostic marker selected from thegroup consisting of a cytokine, growth factor, anti-neutrophil antibody,ASCA, antimicrobial antibody, lactoferrin, anti-tTG antibody, lipocalin,MMP, TIMP, alpha-globulin, actin-severing protein, S100 protein,fibrinopeptide, CGRP, tachykinin, ghrelin, neurotensin,corticotropin-releasing hormone, and combinations thereof.

In other embodiments, the system for first ruling out IBD and thenruling in IBS comprises a data acquisition module configured to producea data set comprising a diagnostic marker profile optionally incombination with a symptom profile which indicates the presence orseverity of at least one symptom in the individual; a data processingmodule configured to process the data set by applying a firststatistical process to the data set to produce a first statisticallyderived decision classifying the sample as an IBD sample or non-IBDsample based upon the diagnostic marker profile and the symptom profile;if the sample is classified as a non-IBD sample, a data processingmodule configured to apply a second statistical process to the same ordifferent data set to produce a second statistically derived decisionclassifying the non-IBD sample as an IBS sample or non-IBS sample; and adisplay module configured to display the first and/or the secondstatistically derived decision.

Other objects, features, and advantages of the present invention will beapparent to one of skill in the art from the following detaileddescription and figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates one embodiment of a molecular pathway derived fromthe IBS markers identified and disclosed herein.

FIG. 2 illustrates a disease classification system (DCS) according toone embodiment of the present invention.

FIG. 3 illustrates a quartile analysis of leptin levels in IBS andnon-IBS patient samples.

FIG. 4, Panel A illustrates the results of an ELISA assay where leptinlevels were measured in IBS-A, IBS-C, and IBS-D patient samples as wellas non-IBS patient samples; Panel B illustrates gender differences inleptin levels for male IBS patients compared to female IBS patients.

FIG. 5 illustrates a quartile analysis of TWEAK levels in IBS andnon-IBS patient samples.

FIG. 6 illustrates a quartile analysis (FIG. 6A) and cumulative percenthistogram analysis (FIG. 6B) of IL-8 levels in IBS and non-IBS patientsamples. Dot plot distribution with bars=median±interquartile rangedisplaying 25%, 50%, and 75% distributions of each patient population.

FIG. 7 illustrates a second cumulative percent histogram analysis ofIL-8 levels in IBS and non-IBS patient samples.

FIG. 8 illustrates the results of an ELISA assay where IL-8 levels weremeasured in IBS-A, IBS-C, and IBS-D patient samples as well as healthycontrol patient samples.

FIG. 9 illustrates a quartile analysis (FIG. 9A) and cumulative percenthistogram analysis (FIG. 9B) of EGF levels in IBS and non-IBS patientsamples. Dot plot distribution with bars=median±i interquartile rangedisplaying 25%, 50%, and 75% distributions of each patient population.

FIG. 10 illustrates a quartile analysis of NGAL levels in IBS andnon-IBS patient samples.

FIG. 11 illustrates a quartile analysis of MMP-9 levels in IBS andnon-IBS patient samples.

FIG. 12 illustrates a quartile analysis of NGAL/MMP-9 complex levels inIBS and non-IBS patient samples.

FIG. 13 illustrates a quartile analysis of Substance P levels in IBS andnon-IBS patient samples.

FIG. 14 illustrates a cumulative percent histogram analysis usinglactoferrin as a non-limiting example.

FIG. 15 illustrates a flow diagram for a sample model algorithm used forclassifying IBS.

FIG. 16 illustrates the data set obtained using the model of FIG. 15.

FIG. 17 illustrates one embodiment of a neural network.

FIG. 18 illustrates the distribution of IBS and non-IBS samples beforeand after modeling with a random forest algorithm. 0=Non-IBS; 1=IBS.

FIG. 19 illustrates one embodiment of a classification tree.

DETAILED DESCRIPTION OF THE INVENTION

I. Introduction

Diagnosing a patient as having irritable bowel syndrome (IBS) can bechallenging due to the similarity in symptoms between IBS and otherdiseases or disorders. For example, patients who have inflammatory boweldisease (IBD), but who exhibit mild signs and symptoms such as bloating,diarrhea, constipation, and abdominal pain can be difficult todistinguish from patients with IBS. As a result, the similarity insymptoms between IBS and IBD renders rapid and accurate diagnosisdifficult and hampers early and effective treatment of the disease.

The present invention is based, in part, upon the surprising discoverythat the accuracy of classifying a biological sample from an individualas an IBS sample can be substantially improved by detecting the presenceor level of certain diagnostic markers (e.g., cytokines, growth factors,anti-neutrophil antibodies, anti-Saccharomyces cerevisiae antibodies,antimicrobial antibodies, lactoferrin, etc.), alone or in combinationwith identifying the presence or severity of IBS-related symptoms basedupon the individual's response to one or more questions (e.g., “Are youcurrently experiencing any symptoms?”). FIG. 1 shows a non-limitingexample of a molecular pathway derived from the IBS markers identifiedand disclosed herein. In some aspects, the present invention usesstatistical algorithms to aid in the classification of a sample as anIBS sample or non-IBS sample. In other aspects, the present inventionuses statistical algorithms for ruling out other intestinal disorders(e.g., IBD), and then classifying the non-IBD sample to aid in theclassification of IBS.

II. Definitions

As used herein, the following terms have the meanings ascribed to themunless specified otherwise.

The term “classifying” includes “to associate” or “to categorize” asample with a disease state. In certain instances, “classifying” isbased on statistical evidence, empirical evidence, or both. In certainembodiments, the methods and systems of classifying use a so-calledtraining set of samples having known disease states. Once established,the training data set serves as a basis, model, or template againstwhich the features of an unknown sample are compared, in order toclassify the unknown disease state of the sample. In certain instances,classifying the sample is akin to diagnosing the disease state of thesample. In certain other instances, classifying the sample is akin todifferentiating the disease state of the sample from another diseasestate.

The term “irritable bowel syndrome” or “IBS” includes a group offunctional bowel disorders characterized by one or more symptomsincluding, but not limited to, abdominal pain, abdominal discomfort,change in bowel pattern, loose or more frequent bowel movements,diarrhea, and constipation, typically in the absence of any apparentstructural abnormality. There are at least three forms of IBS, dependingon which symptom predominates: (1) diarrhea-predominant (IBS-D); (2)constipation-predominant (IBS-C); and (3) IBS with alternating stoolpattern (IBS-A). IBS can also occur in the form of a mixture of symptoms(IBS-M). There are also various clinical subtypes of IBS, such aspost-infectious IBS (IBS-PI).

The term “sample” includes any biological specimen obtained from anindividual. Suitable samples for use in the present invention include,without limitation, whole blood, plasma, serum, saliva, urine, stool,sputum, tears, any other bodily fluid, tissue samples (e.g., biopsy),and cellular extracts thereof (e.g., red blood cellular extract). In apreferred embodiment, the sample is a serum sample. The use of samplessuch as serum, saliva, and urine is well known in the art (see, e.g.,Hashida et al., J. Clin. Lab. Anal., 11:267-86 (1997)). One skilled inthe art will appreciate that samples such as serum samples can bediluted prior to the analysis of marker levels.

The term “biomarker” or “marker” includes any diagnostic marker such asa biochemical marker, serological marker, genetic marker, or otherclinical or echographic characteristic that can be used to classify asample from an individual as an IBS sample or to rule out one or morediseases or disorders associated with IBS-like symptoms in a sample froman individual. The term “biomarker” or “marker” also encompasses anyclassification marker such as a biochemical marker, serological marker,genetic marker, or other clinical or echographic characteristic that canbe used to classify IBS into one of its various forms or clinicalsubtypes. Non-limiting examples of diagnostic markers suitable for usein the present invention are described below and include cytokines,growth factors, anti-neutrophil antibodies, anti-Saccharomycescerevisiae antibodies, antimicrobial antibodies, anti-tissuetransglutaminase (tTG) antibodies, lipocalins, matrix metalloproteinases(MMPs), tissue inhibitor of metalloproteinases (TIMPs), alpha-globulins,actin-severing proteins, S100 proteins, fibrinopeptides, calcitoningene-related peptide (CGRP), tachykinins, ghrelin, neurotensin,corticotropin-releasing hormone (CRH), elastase, C-reactive protein(CRP), lactoferrin, anti-lactoferrin antibodies, calprotectin,hemoglobin, NOD2/CARD 15, serotonin reuptake transporter (SERT),tryptophan hydroxylase-1, 5-hydroxytryptamine (5-HT), lactulose, and thelike. Examples of classification markers include, without limitation,leptin, SERT, tryptophan hydroxylase-1, 5-HT, antrum mucosal protein 8,keratin-8, claudin-8, zonulin, corticotropin releasing hormonereceptor-1 (CRHR1), corticotropin releasing hormone receptor-2 (CRHR2),and the like. In some embodiments, diagnostic markers can be used toclassify IBS into one of its various forms or clinical subtypes. Inother embodiments, classification markers can be used to classify asample as an IBS sample or to rule out one or more diseases or disordersassociated with IBS-like symptoms. One skilled in the art will know ofadditional diagnostic and classification markers suitable for use in thepresent invention.

As used herein, the term “profile” includes any set of data thatrepresents the distinctive features or characteristics associated with adisease or disorder such as IBS or IBD. The term encompasses a“diagnostic marker profile” that analyzes one or more diagnostic markersin a sample, a “symptom profile” that identifies one or more IBS-relatedclinical factors (i.e., symptoms) an individual is experiencing or hasexperienced, and combinations thereof. For example, a “diagnostic markerprofile” can include a set of data that represents the presence or levelof one or more diagnostic markers associated with IBS and/or IBD.Likewise, a “symptom profile” can include a set of data that representsthe presence, seventy, frequency, and/or duration of one or moresymptoms associated with IBS and/or IBD.

The term “individual,” “subject,” or “patient” typically refers tohumans, but also to other animals including, e.g., other primates,rodents, canines, felines, equines, ovines, porcines, and the like.

As used herein, the term “substantially the same amino acid sequence”includes an amino acid sequence that is similar, but not identical to,the naturally-occurring amino acid sequence. For example, an amino acidsequence that has substantially the same amino acid sequence as anaturally-occurring peptide, polypeptide, or protein can have one ormore modifications such as amino acid additions, deletions, orsubstitutions relative to the amino acid sequence of thenaturally-occurring peptide, polypeptide, or protein, provided that themodified sequence retains substantially at least one biological activityof the naturally-occurring peptide, polypeptide, or protein such asimmunoreactivity. Comparison for substantial similarity between aminoacid sequences is usually performed with sequences between about 6 and100 residues, preferably between about 10 and 100 residues, and morepreferably between about 25 and 35 residues. A particularly usefulmodification of a peptide, polypeptide, or protein of the presentinvention, or a fragment thereof, is a modification that confers, forexample, increased stability. Incorporation of one or more D-amino acidsis a modification useful in increasing stability of a polypeptide orpolypeptide fragment. Similarly, deletion or substitution of lysineresidues can increase stability by protecting the polypeptide orpolypeptide fragment against degradation.

The term “monitoring the progression or regression of IBS” includes theuse of the methods, systems, and code of the present invention todetermine the disease state (e.g., presence or severity of IBS) of anindividual. In certain instances, the results of an algorithm (e.g., alearning statistical classifier system) are compared to those resultsobtained for the same individual at an earlier time. In someembodiments, the methods, systems, and code of the present invention canbe used to predict the progression of IBS, e.g., by determining alikelihood for IBS to progress either rapidly or slowly in an individualbased on an analysis of diagnostic markers and/or the identification orIBS-related symptoms. In other embodiments, the methods, systems, andcode of the present invention can be used to predict the regression ofIBS, e.g., by determining a likelihood for IBS to regress either rapidlyor slowly in an individual based on an analysis of diagnostic markersand/or the identification or IBS-related symptoms.

The term “monitoring drug efficacy in an individual receiving a druguseful for treating IBS” includes the use of the methods, systems, andcode of the present invention to determine the effectiveness of atherapeutic agent for treating IBS after it has been administered. Incertain instances, the results of an algorithm (e.g., a learningstatistical classifier system) are compared to those results obtainedfor the same individual before initiation of use of the therapeuticagent or at an earlier time in therapy. As used herein, a drug usefulfor treating IBS is any compound or drug used to improve the health ofthe individual and includes, without limitation, IBS drugs such asserotonergic agents, antidepressants, chloride channel activators,chloride channel blockers, guanylate cyclase agonists, antibiotics,opioids, neurokinin antagonists, antispasmodic or anticholinergicagents, belladonna alkaloids, barbiturates, glucagon-like peptide-1(GLP-1) analogs, corticotropin releasing factor (CRF) antagonists,probiotics, free bases thereof, pharmaceutically acceptable saltsthereof, derivatives thereof, analogs thereof, and combinations thereof.

The term “therapeutically effective amount or dose” includes a dose of adrug that is capable of achieving a therapeutic effect in a subject inneed thereof. For example, a therapeutically effective amount of a druguseful for treating IBS can be the amount that is capable of preventingor relieving one or more symptoms associated with IBS. The exact amountcan be ascertainable by one skilled in the art using known techniques(see, e.g., Lieberman, Pharmaceutical Dosage Forms, Vols. 1-3 (1992);Lloyd, The Art, Science and Technology of Pharmaceutical Compounding(1999); Pickar, Dosage Calculations (1999); and Remington: The Scienceand Practice of Pharmacy, 20th Edition, Gennaro, Ed., Lippincott,Williams & Wilkins (2003)).

III. Description of the Embodiments

The present invention provides methods, systems, and code for accuratelyclassifying whether a sample from an individual is associated withirritable bowel syndrome (IBS). In some embodiments, the presentinvention is useful for classifying a sample from an individual as anIBS sample using a statistical algorithm (e.g., a learning statisticalclassifier system) and/or empirical data (e.g., the presence or level ofan IBS marker). The present invention is also useful for ruling out oneor more diseases or disorders that present with IBS-like symptoms andruling in IBS using a combination of statistical algorithms and/orempirical data. Accordingly, the present invention provides an accuratediagnostic prediction of IBS and prognostic information useful forguiding treatment decisions.

In one aspect, the present invention provides a method for classifyingwhether a sample from an individual is associated with IBS, the methodcomprising:

-   -   (a) determining a diagnostic marker profile by detecting the        presence or level of at least one diagnostic marker in the        sample; and    -   (b) classifying the sample as an IBS sample or non-IBS sample        using an algorithm based upon the diagnostic marker profile.

In some embodiments, the diagnostic marker profile is determined bydetecting the presence or level of at least one diagnostic markerselected from the group consisting of a cytokine, growth factor,anti-neutrophil antibody, anti-Saccharomyces cerevisiae antibody (ASCA),antimicrobial antibody, lactoferrin, anti-tissue transglutaminase (tTG)antibody, lipocalin, matrix metalloproteinase (MMP), tissue inhibitor ofmetalloproteinase (TIMP), alpha-globulin, actin-severing protein, S100protein, fibrinopeptide, calcitonin gene-related peptide (CGRP),tachykinin, ghrelin, neurotensin, corticotropin-releasing hormone, andcombinations thereof.

In other embodiments, the presence or level of at least two, three,four, five, six, seven, eight, nine, ten, or more diagnostic markers aredetermined in the individual's sample. In certain instances, thecytokine comprises one or more of the cytokines described below.Preferably, the presence or level of IL-8, IL-1β, TNF-related weakinducer of apoptosis (TWEAK), leptin, osteoprotegerin (OPG), MIP-3β,GROα, CXCL4/PF-4, and/or CXCL7/NAP-2 is determined in the individual'ssample. In certain other instances, the growth factor comprises one ormore of the growth factors described below. Preferably, the presence orlevel of epidermal growth factor (EGF), vascular endothelial growthfactor (VEGF), piginent epithelium-derived factor (PEDF), brain-derivedneurotrophic factor (BDNF), and/or amphiregulin (SDGF) is determined inthe individual's sample.

In some instances, the anti-neutrophil antibody comprises ANCA, pANCA,cANCA, NSNA, SAPPA, and combinations thereof. In other instances, theASCA comprises ASCA-IgA, ASCA-IgG, ASCA-IgM, and combinations thereof.In further instances, the antimicrobial antibody comprises an anti-OmpCantibody, anti-flagellin antibody, anti-I2 antibody, and combinationsthereof.

In certain instances, the lipocalin comprises one or more of thelipocalins described below. Preferably, the presence or level ofneutrophil gelatinase-associated lipocalin (NGAL) and/or a complex ofNGAL and a matrix metalloproteinase (e.g., NGAL/MMP-9 complex) isdetermined in the individual's sample. In other instances, the matrixmetalloproteinase (MMP) comprises one or more of the MMPs describedbelow. Preferably, the presence or level of MMP-9 is determined in theindividual's sample. In further instances, the tissue inhibitor ofmetalloproteinase (TIMP) comprises one or more of the TIMPs describedbelow. Preferably, the presence or level of TIMP-1 is determined in theindividual's sample. In yet further instances, the alpha-globulincomprises one or more of the alpha-globulins described below.Preferably, the presence or level of alpha-2-macroglobulin, haptoglobin,and/or orosomucoid is determined in the individual's sample.

In certain other instances, the actin-severing protein comprises one ormore of the actin-severing protein described below. Preferably, thepresence or level of gelsolin is determined in the individual's sample.In additional instances, the S100 protein comprises one or more of theS100 proteins described below including, for example, calgranulin. Inyet other instances, the fibrinopeptide comprises one or more of thefibrinopeptides described below. Preferably, the presence or level offibrinopeptide A (FIBA) is determined in the individual's sample. Infurther instances, the presence or level of a tachykinin such asSubstance P, neurokinin A, and/or neurokinin B is detennined in theindividual's sample. The presence or level of other diagnostic markerssuch as, for example, anti-lactoferrin antibody, L-selectin/CD62L,elastase, C-reactive protein (CRP), calprotectin, anti-U1-70 kDaautoantibody, zona occludens 1 (ZO-1), vasoactive intestinal peptide(VIP), serum amyloid A, and/or gastrin can also be determined.

The sample used for detecting or determining the presence or level of atleast one diagnostic marker is typically whole blood, plasma, serum,saliva, urine, stool (i.e., feces), tears, and any other bodily fluid,or a tissue sample (i.e., biopsy) such as a small intestine or colonsample. Preferably, the sample is serum, whole blood, plasma, stool,urine, or a tissue biopsy. In certain instances, the methods of thepresent invention further comprise obtaining the sample from theindividual prior to detecting or determining the presence or level of atleast one diagnostic marker in the sample.

In some embodiments, a panel for measuring one or more of the diagnosticmarkers described above may be constructed and used for classifying thesample as an IBS sample or non-IBS sample. One skilled in the art willappreciate that the presence or level of a plurality of diagnosticmarkers can be determined simultaneously or sequentially, using, forexample, an aliquot or dilution of the individual's sample. In certaininstances, the level of a particular diagnostic marker in theindividual's sample is considered to be elevated when it is at leastabout 25%, 50%, 75%, 100%, 125%, 150%, 175%, 200%, 250%, 300%, 350%,400%, 450%, 500%, 600%, 700%, 800%, 900%, or 1000% greater than thelevel of the same marker in a comparative sample (e.g., a normal, GIcontrol, IBD, and/or Celiac disease sample) or population of samples(e.g., greater than a median level of the same marker in a comparativepopulation of normal, GI control, IBD, and/or Celiac disease samples).In certain other instances, the level of a particular diagnostic markerin the individual's sample is considered to be lowered when it is atleast about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%,65%, 70%, 75%, 80%, 85%, 90%, or 95% less than the level of the samemarker in a comparative sample (e.g., a normal, GI control, IBD, and/orCeliac disease sample) or population of samples (e.g., less than amedian level of the same marker in a comparative population of normal,GI control, IBD, and/or Celiac disease samples).

In certain embodiments, the presence or level of at least one diagnosticmarker is determined using an assay such as a hybridization assay or anamplification-based assay. Examples of hybridization assays suitable foruse in the methods of the present invention include, but are not limitedto, Northern blotting, dot blotting, RNase protection, and a combinationthereof. A non-limiting example of an amplification-based assay suitablefor use in the methods of the present invention includes a reversetranscriptase-polymerase chain reaction (RT-PCR).

In certain other embodiments, the presence or level of at least onediagnostic marker is determined using an immunoassay or animmunohistochemical assay. A non-limiting example of an immunoassaysuitable for use in the methods of the present invention includes anenzyme-linked immunosorbent assay (ELISA). Examples ofimmunohistochemical assays suitable for use in the methods of thepresent invention include, but are not limited to, immunofluorescenceassays such as direct fluorescent antibody assays, indirect fluorescentantibody (IFA) assays, anticomplement immunofluorescence assays, andavidin-biotin immunofluorescence assays. Other types ofimmunohistochemical assays include immunoperoxidase assays.

In some embodiments, the method of ruling in IBS comprises determining adiagnostic marker profile optionally in combination with a symptomprofile, wherein the symptom profile is determined by identifying thepresence or severity of at least one symptom in the individual; andclassifying the sample as an IBS sample or non-IBS sample using analgorithm based upon the diagnostic marker profile and the symptomprofile. One skilled in the art will appreciate that the diagnosticmarker profile and the symptom profile can be determined simultaneouslyor sequentially in any order.

The symptom profile is typically determined by identifying the presenceor severity of at least one symptom selected from the group consistingof chest pain, chest discomfort, heartburn, uncomfortable fullness afterhaving a regular-sized meal, inability to finish a regular-sized meal,abdominal pain, abdominal discomfort, constipation, diarrhea, bloating,abdominal distension, negative thoughts or feelings associated withhaving pain or discomfort, and combinations thereof.

In preferred embodiments, the presence or severity of 1, 2, 3, 4, 5, 6,7, 8, 9, 10, or more of the symptoms described herein is identified togenerate a symptom profile that is useful for predicting IBS. In certaininstances, a questionnaire or other form of written, verbal, ortelephone survey is used to produce the symptom profile. Thequestionnaire or survey typically comprises a standardized set ofquestions and answers for the purpose of gathering information fromrespondents regarding their current and/or recent IBS-related symptoms.For instance, Example 13 provides exemplary questions that can beincluded in a questionnaire for identifying the presence or severity ofone or more IBS-related symptoms in the individual.

In certain embodiments, the symptom profile is produced by compilingand/or analyzing all or a subset of the answers to the questions setforth in the questionnaire or survey. In certain other embodiments, thesymptom profile is produced based upon the individual's response to thefollowing question: “Are you currently experiencing any symptoms?” Thesymptom profile generated in accordance with either of these embodimentscan be used in combination with a diagnostic marker profile in thealgorithmic-based methods described herein to improve the accuracy ofpredicting IBS.

In some embodiments, classifying a sample as an IBS sample or non-IBSsample is based upon the diagnostic marker profile, alone or incombination with a symptom profile, in conjunction with a statisticalalgorithm. In certain instances, the statistical algorithm is a learningstatistical classifier system. The learning statistical classifiersystem can be selected from the group consisting of a random forest(RF), classification and regression tree (C&RT), boosted tree, neuralnetwork (NN), support vector machine (SVM), general chi-squaredautomatic interaction detector model, interactive tree, multiadaptiveregression spline, machine learning classifier, and combinationsthereof. Preferably, the learning statistical classifier system is atree-based statistical algorithm (e.g., RF, C&RT, etc.) and/or a NN(e.g., artificial NN, etc.). Additional examples of learning statisticalclassifier systems suitable for use in the present invention aredescribed in U.S. patent application Ser. No. 11/368,285.

In certain instances, the statistical algorithm is a single learningstatistical classifier system. Preferably, the single learningstatistical classifier system comprises a tree-based statisticalalgorithm such as a RF or C&RT. As a non-limiting example, a singlelearning statistical classifier system can be used to classify thesample as an IBS sample or non-IBS sample based upon a prediction orprobability value and the presence or level of at least one diagnosticmarker (i.e., diagnostic marker profile), alone or in combination withthe presence or severity of at least one symptom (i.e., symptomprofile). The use of a single learning statistical classifier systemtypically classifies the sample as an IBS sample with a sensitivity,specificity, positive predictive value, negative predictive value,and/or overall accuracy of at least about 75%, 76%, 77%, 78%, 79%, 80%,81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%,95%, 96%, 97%, 98%, or 99%.

In certain other instances, the statistical algorithm is a combinationof at least two learning statistical classifier systems. Preferably, thecombination of learning statistical classifier systems comprises a RFand a NN, e.g., used in tandem or parallel. As a non-limiting example, aRF can first be used to generate a prediction or probability value basedupon the diagnostic marker profile, alone or in combination with asymptom profile, and a NN can then be used to classify the sample as anIBS sample or non-IBS sample based upon the prediction or probabilityvalue and the same or different diagnostic marker profile or combinationof profiles. Advantageously, the hybrid RF/NN learning statisticalclassifier system of the present invention classifies the sample as anIBS sample with a sensitivity, specificity, positive predictive value,negative predictive value, and/or overall accuracy of at least about75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%,89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

In some instances, the data obtained from using the learning statisticalclassifier system or systems can be processed using a processingalgorithm. Such a processing algorithm can be selected, for example,from the group consisting of a multilayer perceptron, backpropagationnetwork, and Levenberg-Marquardt algorithm. In other instances, acombination of such processing algorithms can be used, such as in aparallel or serial fashion.

In certain embodiments, the methods of the present invention furthercomprise classifying the non-IBS sample as a normal, inflammatory boweldisease (IBD), or non-IBD sample. Classification of the non-IBS samplecan be performed, for example, using at least one of the diagnosticmarkers described above.

In certain other embodiments, the methods of the present inventionfurther comprise sending the IBS classification results to a clinician,e.g., a gastroenterologist or a general practitioner. In anotherembodiment, the methods of the present invention provide a diagnosis inthe form of a probability that the individual has IBS. For example, theindividual can have about a 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%,45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or greaterprobability of having IBS. In yet another embodiment, the methods of thepresent invention further provide a prognosis of IBS in the individual.For example, the prognosis can be surgery, development of a category orclinical subtype of IBS, development of one or more symptoms, orrecovery from the disease.

In some embodiments, the diagnosis of an individual as having IBS isfollowed by administering to the individual a therapeutically effectiveamount of a drug useful for treating one or more symptoms associatedwith IBS. Suitable IBS drugs include, but are not limited to,serotonergic agents, antidepressants, chloride channel activators,chloride channel blockers, guanylate cyclase agonists, antibiotics,opioid agonists, neurokinin antagonists, antispasmodic oranticholinergic agents, belladonna alkaloids, barbiturates, GLP-1analogs, CRF antagonists, probiotics, free bases thereof,pharmaceutically acceptable salts thereof, derivatives thereof, analogsthereof, and combinations thereof. Other IBS drugs include bulkingagents, dopamine antagonists, carminatives, tranquilizers, dextofisopam,phenytoin, timolol, and diltiazem. Additionally, amino acids likeglutamine and glutamic acid which regulate intestinal permeability byaffecting neuronal or glial cell signaling can be administered to treatpatients with IBS.

In other embodiments, the methods of the present invention furthercomprise classifying the IBS sample as an IBS-constipation (IBS-C),IBS-diarrhea (IBS-D), IBS-mixed (IBS-M), IBS-alternating (IBS-A), orpost-infectious IBS (IBS-PI) sample. In certain instances, theclassification of the IBS sample into a category, form, or clinicalsubtype of IBS is based upon the presence or level of at least one, two,three, four, five, six, seven, eight, nine, ten, or more classificationmarkers. Non-limiting examples of classification markers are describedbelow. Preferably, at least one form of IBS is distinguished from atleast one other form of IBS based upon the presence or level of leptin.In certain instances, the methods of the present invention can be usedto differentiate an IBS-C sample from an IBS-A and/or IBS-D sample in anindividual previously identified as having IBS. In certain otherinstances, the methods of the present invention can be used to classifya sample from an individual not previously diagnosed with IBS as anIBS-A sample, IBS-C sample, IBS-D sample, or non-IBS sample.

In certain embodiments, the methods further comprise sending the resultsfrom the classification to a clinician. In certain other embodiments,the methods further provide a diagnosis in the form of a probabilitythat the individual has IBS-A, IBS-C, IBS-D, IBS-M, or IBS-PI. Themethods of the present invention can further comprise administering tothe individual a therapeutically effective amount of a drug useful fortreating IBS-A, IBS-C, IBS-D, IBS-M, or IBS-PI. Suitable drugs include,but are not limited to, tegaserod (Zelnorm™), alosetron (Lotronex®),lubiprostone (Amitiza™), rifamixin (Xifaxan™), MD-1100, probiotics, anda combination thereof. In instances where the sample is classified as anIBS-A or IBS-C sample and/or the individual is diagnosed with IBS-A orIBS-C, a therapeutically effective dose of tegaserod or other 5-HT₄agonist (e.g., mosapride, renzapride, AG1-001, etc.) can be administeredto the individual. In some instances, when the sample is classified asIBS-C and/or the individual is diagnosed with IBS-C, a therapeuticallyeffective amount of lubiprostone or other chloride channel activator,rifamixin or other antibiotic capable of controlling intestinalbacterial overgrowth, MD-1100 or other guanylate cyclase agonist,asimadoline or other opioid agonist, or talnetant or other neurokininantagonist can be administered to the individual. In other instances,when the sample is classified as IBS-D and/or the individual isdiagnosed with IBS-D, a therapeutically effective amount of alosetron orother 5-HT₃ antagonist (e.g., ramosetron, DDP-225, etc.), crofelemer orother chloride channel blocker, talnetant or other neurokinin antagonist(e.g., saredutant, etc.), or an antidepressant such as a tricyclicantidepressant can be administered to the individual.

In additional embodiments, the methods of the present invention furthercomprise ruling out intestinal inflammation. Non-limiting examples ofintestinal inflammation include acute inflammation, diverticulitis,ileal pouch-anal anastomosis, microscopic colitis, infectious diarrhea,and combinations thereof. In some instances, the intestinal inflammationis ruled out based upon the presence or level of C-reactive protein(CRP), lactoferrin, calprotectin, or combinations thereof.

In another aspect, the present invention provides a method forclassifying whether a sample from an individual is associated with IBS,the method comprising:

-   -   (a) determining a diagnostic marker profile by detecting the        presence or level of at least one diagnostic marker in the        sample;    -   (b) classifying the sample as an IBD sample or non-IBD sample        using a first statistical algorithm based upon the diagnostic        marker profile; and    -   if the sample is classified as a non-IBD sample,    -   (c) classifying the non-IBD sample as an IBS sample or non-IBS        sample using a second statistical algorithm based upon the same        diagnostic marker profile as determined in step (a) or a        different diagnostic marker profile.

In some embodiments, the diagnostic marker profile is determined bydetecting the presence or level of at least one, two, three, four, five,six, seven, eight, nine, ten, or more diagnostic markers selected fromthe group consisting of a cytokine (e.g., IL-8, IL-1β, TWEAK, leptin,OPG, MIP-3β, GROα, CXCL4/PF-4, and/or CXCL7/NAP-2), growth factor (e.g.,EGF, VEGF, PEDF, BDNF, and/or SDGF), anti-neutrophil antibody (e.g.,ANCA, pANCA, cANCA, NSNA, and/or SAPPA), ASCA (e.g., ASCA-IgA, ASCA-IgG,and/or ASCA-IgM), antimicrobial antibody (e.g., anti-OmpC antibody,anti-flagellin antibody, and/or anti-I2 antibody), lactoferrin, anti-tTGantibody, lipocalin (e.g., NGAL, NGAL/MMP-9 complex), MMP (e.g., MMP-9),TIMP (e.g., TIMP-1), alpha-globulin (e.g., alpha-2-macroglobulin,haptoglobin, and/or orosomucoid), actin-severing protein (e.g.,gelsolin), S100 protein (e.g., calgranulin), fibrinopeptide (e.g.,FIBA), CGRP, tachykinin (e.g., Substance P), ghrelin, neurotensin,corticotropin-releasing hormone, and combinations thereof. The presenceor level of other diagnostic markers such as, for example,anti-lactoferrin antibody, L-selectin/CD62L, elastase, C-reactiveprotein (CRP), calprotectin, anti-U1-70 kDa autoantibody, zona occludens1 (ZO-1), vasoactive intestinal peptide (VIP), serum amyloid A, and/orgastrin can also be determined.

The diagnostic markers used for ruling out IBD can be the same as thediagnostic markers used for ruling in IBS. Alternatively, the diagnosticmarkers used for ruling out IBD can be different than the diagnosticmarkers used for ruling in IBS.

The sample used for detecting or determining the presence or level of atleast one diagnostic marker is typically whole blood, plasma, serum,saliva, urine, stool (i.e., feces), tears, and any other bodily fluid,or a tissue sample (i.e., biopsy) such as a small intestine or colonsample. Preferably, the sample is serum, whole blood, plasma, stool,urine, or a tissue biopsy. In certain instances, the methods of thepresent invention further comprise obtaining the sample from theindividual prior to detecting or determining the presence or level of atleast one diagnostic marker in the sample.

In some embodiments, a panel for measuring one or more of the diagnosticmarkers described above may be constructed and used for ruling out IBDand/or ruling in IBS. One skilled in the art will appreciate that thepresence or level of a plurality of diagnostic markers can be determinedsimultaneously or sequentially, using, for example, an aliquot ordilution of the individual's sample. As described above, the level of aparticular diagnostic marker in the individual's sample is generallyconsidered to be elevated when it is at least about 25%, 50%, 75%, 100%,125%, 150%, 175%, 200%, 250%, 300%, 350%, 400%, 450%, 500%, 600%, 700%,800%, 900%, or 1000% greater than the level of the same marker in acomparative sample or population of samples (e.g., greater than a medianlevel). Similarly, the level of a particular diagnostic marker in theindividual's sample is typically considered to be lowered when it is atleast about 5%,10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%,65%, 70%, 75%, 80%, 85%, 90%, or 95% less than the level of the samemarker in a comparative sample or population of samples (e.g., less thana median level).

In certain instances, the presence or level of at least one diagnosticmarker is determined using an assay such as a hybridization assay or anamplification-based assay. Examples of hybridization assays andamplification-based assays suitable for use in the methods of thepresent invention are described above. In certain other instances, thepresence or level of at least one diagnostic marker is determined usingan immunoassay or an immunohistochemical assay. Non-limiting examples ofimmunoassays and immunohistochemical assays suitable for use in themethods of the present invention are described above.

In some embodiments, the method of first ruling out IBD (i.e.,classifying the sample as an IBD sample or non-IBD sample) and thenruling in IBS (i.e., classifying the non-IBD sample as an IBS sample ornon-IBS sample) comprises determining a diagnostic marker profileoptionally in combination with a symptom profile, wherein the symptomprofile is determined by identifying the presence or severity of atleast one symptom in the individual; classifying the sample as an IBDsample or non-IBD sample using a first statistical algorithm based uponthe diagnostic marker profile and the symptom profile; and if the sampleis classified as a non-IBD sample, classifying the non-IBD sample as anIBS sample or non-IBS sample using a second statistical algorithm basedupon the same profiles as determined in step (a) or different profiles.One skilled in the art will appreciate that the diagnostic markerprofile and the symptom profile can be determined simultaneously orsequentially in any order.

In other embodiments, the first statistical algorithm is a learningstatistical classifier system selected from the group consisting of arandom forest (RF), classification and regression tree (C&RT), boostedtree, neural network (NN), support vector machine (SVM), generalchi-squared automatic interaction detector model, interactive tree,multiadaptive regression spline, machine learning classifier, andcombinations thereof. In certain instances, the first statisticalalgorithm is a single learning statistical classifier system.Preferably, the single learning statistical classifier system comprisesa tree-based statistical algorithm such as a RF or C&RT. In certainother instances, the first statistical algorithm is a combination of atleast two learning statistical classifier systems, e.g., used in tandemor parallel. As a non-limiting example, a RF can first be used togenerate a prediction or probability value based upon the diagnosticmarker profile, alone or in combination with a symptom profile, and a NN(e.g., artificial NN) can then be used to classify the sample as anon-IBD sample or IBD sample based upon the prediction or probabilityvalue and the same or different diagnostic marker profile or combinationof profiles. The hybrid RF/NN learning statistical classifier system ofthe present invention typically classifies the sample as a non-IBDsample with a sensitivity, specificity, positive predictive value,negative predictive value, and/or overall accuracy of at least about75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%,89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

In yet other embodiments, the second statistical algorithm comprises anyof the learning statistical classifier systems described above. Incertain instances, the second statistical algorithm is a single learningstatistical classifier system such as, for example, a tree-basedstatistical algorithm (e.g., RF or C&RT). In certain other instances,the second statistical algorithm is a combination of at least twolearning statistical classifier systems, e.g., used in tandem orparallel. As a non-limiting example, a RF can first be used to generatea prediction or probability value based upon the diagnostic markerprofile, alone or in combination with a symptom profile, and a NN (e.g.,artificial NN) or SVM can then be used to classify the non-IBD sample asa non-IBS sample or IBS sample based upon the prediction or probabilityvalue and the same or different diagnostic marker profile or combinationof profiles. The hybrid RF/NN or RF/SVM learning statistical classifiersystem described herein typically classifies the sample as an IBS samplewith a sensitivity, specificity, positive predictive value, negativepredictive value, and/or overall accuracy of at least about 75%, 76%,77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%,91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

In some instances, the data obtained from using the learning statisticalclassifier system or systems can be processed using a processingalgorithm. Such a processing algorithm can be selected, for example,from the group consisting of a multilayer perceptron, backpropagationnetwork, and Levenberg-Marquardt algorithm. In other instances, acombination of such processing algorithms can be used, such as in aparallel or serial fashion.

As described above, the methods of the present invention can furthercomprise sending the IBS classification results to a clinician, e.g., agastroenterologist or a general practitioner. The methods can alsoprovide a diagnosis in the form of a probability that the individual hasIBS. For example, the individual can have about a 0%, 5%, 10%, 15%, 20%,25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%,95%, or greater probability of having IBS. In some instances, themethods of the present invention further provide a prognosis of IBS inthe individual. For example, the prognosis can be surgery, developmentof a category or clinical subtype of IBS, development of one or moresymptoms, or recovery from the disease.

In some embodiments, the diagnosis of an individual as having IBS isfollowed by administering to the individual a therapeutically effectiveamount of a drug useful for treating one or more symptoms associatedwith IBS. Suitable IBS drugs are described above.

In other embodiments, the methods of the present invention furthercomprise classifying the IBS sample as an IBS-A, IBS-C, IBS-D, IBS-M, orIBS-PI sample. In certain instances, the classification of the IBSsample into a category, form, or clinical subtype of IBS is based uponthe presence or level of at least one classification marker.Non-limiting examples of classification markers are described below.Preferably, at least one form of IBS is distinguished from at least oneother form of IBS based upon the presence or level of leptin. Theresults from the classification can be sent to a clinician. In someinstances, the methods can further provide a diagnosis in the form of aprobability that the individual has IBS-A, IBS-C, IBS-D, IBS-M, orIBS-PI. In other instances, the methods can further compriseadministering to the individual a therapeutically effective amount of adrug useful for treating IBS-A, IBS-C, IBS-D, IBS-M, or IBS-PI such as,for example, tegaserod (Zelnorm™), alosetron (Lotronex®), lubiprostone(Amitiza™), rifamixin (Xifaxan™), MD-1100, probiotics, and combinationsthereof.

In additional embodiments, the methods of the present invention furthercomprise ruling out intestinal inflammation. Non-limiting examples ofintestinal inflammation are described above. In certain instances, theintestinal inflammation is ruled out based upon the presence or level ofCRP, lactoferrin, and/or calprotectin.

In yet another aspect, the present invention provides a method formonitoring the progression or regression of IBS in an individual, themethod comprising:

-   -   (a) determining a diagnostic marker profile by detecting the        presence or level of at least one diagnostic marker in the        sample; and    -   (b) determining the presence or severity of IBS in the        individual using an algorithm based upon the diagnostic marker        profile.

In a related aspect, the present invention provides a method formonitoring drug efficacy in an individual receiving a drug useful fortreating IBS, the method comprising:

-   -   (a) determining a diagnostic marker profile by detecting the        presence or level of at least one diagnostic marker in the        sample; and    -   (b) determining the effectiveness of the drug using an algorithm        based upon the diagnostic marker profile.

In some embodiments, the diagnostic marker profile is determined bydetecting the presence or level of at least one, two, three, four, five,six, seven, eight, nine, ten, or more diagnostic markers selected fromthe group consisting of a cytokine (e.g., IL-8, IL-1β, TWEAK, leptin,OPG, MIP-3β, GROα, CXCL4/PF-4, and/or CXCL7/NAP-2), growth factor (e.g.,EGF, VEGF, PEDF, BDNF, and/or SDGF), anti-neutrophil antibody (e.g.,ANCA, pANCA, cANCA, NSNA, and/or SAPPA), ASCA (e.g., ASCA-IgA, ASCA-IgG,and/or ASCA-IgM), antimicrobial antibody (e.g., anti-OmpC antibody,anti-flagellin antibody, and/or anti-I2 antibody), lactoferrin, anti-tTGantibody, lipocalin (e.g., NGAL, NGAL/MMP-9 complex), MMP (e.g., MMP-9),TIMP (e.g., TIMP-1), alpha-globulin (e.g., alpha-2-macroglobulin,haptoglobin, and/or orosomucoid), actin-severing protein (e.g.,gelsolin), S100 protein (e.g., calgranulin), fibrinopeptide (e.g.,FIBA), CGRP, tachykinin (e.g., Substance P), ghrelin, neurotensin,corticotropin-releasing hormone, and combinations thereof. The presenceor level of other diagnostic markers such as, for example,anti-lactoferrin antibody, L-selectin/CD62L, elastase, C-reactiveprotein (CRP), calprotectin, anti-U1-70 kDa autoantibody, zona occludens1 (ZO-1), vasoactive intestinal peptide (VIP), serum amyloid A, and/orgastrin can also be determined.

The sample used for detecting or determining the presence or level of atleast one diagnostic marker is typically whole blood, plasma, serum,saliva, urine, stool (i.e., feces), tears, and any other bodily fluid,or a tissue sample (i.e., biopsy) such as a small intestine or colonsample. Preferably, the sample is serum, whole blood, plasma, stool,urine, or a tissue biopsy. In certain instances, the methods of thepresent invention further comprise obtaining the sample from theindividual prior to detecting or determining the presence or level of atleast one diagnostic marker in the sample.

In some embodiments, a panel for measuring one or more of the diagnosticmarkers described above may be constructed and used for determining thepresence or severity of IBS or for determining the effectiveness of anIBS drug. One skilled in the art will appreciate that the presence orlevel of a plurality of diagnostic markers can be determinedsimultaneously or sequentially, using, for example, an aliquot ordilution of the individual's sample. As described above, the level of aparticular diagnostic marker in the individual's sample is generallyconsidered to be elevated when it is at least about 25%, 50%, 75%, 100%,125%, 150%, 175%, 200%, 250%, 300%, 350%, 400%, 450%, 500%, 600%, 700%,800%, 900%, or 1000% greater than the level of the same marker in acomparative sample or population of samples (e.g., greater than a medianlevel). Similarly, the level of a particular diagnostic marker in theindividual's sample is typically considered to be lowered when it is atleast about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%,65%, 70%, 75%, 80%, 85%, 90%, or 95% less than the level of the samemarker in a comparative sample or population of samples (e.g., less thana median level).

In certain instances, the presence or level of at least one diagnosticmarker is determined using an assay such as a hybridization assay or anamplification-based assay. Examples of hybridization assays andamplification-based assays suitable for use in the methods of thepresent invention are described above. Alternatively, the presence orlevel of at least one diagnostic marker is determined using animmunoassay or an immunohistochemical assay. Non-limiting examples ofimmunoassays and immunohistochemical assays suitable for use in themethods of the present invention are described above.

In certain embodiments, the method of monitoring the progression orregression of IBS comprises determining a diagnostic marker profileoptionally in combination with a symptom profile, wherein the symptomprofile is determined by identifying the presence or severity of atleast one symptom in the individual; and determining the presence orseverity of IBS in the individual using an algorithm based upon thediagnostic marker profile and the symptom profile. In certain otherembodiments, the method of monitoring IBS drug efficacy comprisesdetermining a diagnostic marker profile optionally in combination with asymptom profile, wherein the symptom profile is determined byidentifying the presence or severity of at least one symptom in theindividual; and determining the effectiveness of the drug using analgorithm based upon the diagnostic marker profile and the symptomprofile. One skilled in the art will appreciate that the diagnosticmarker profile and the symptom profile can be determined simultaneouslyor sequentially in any order.

In some embodiments, determining the presence or severity of IBS or theeffectiveness of an IBS drug is based upon the diagnostic markerprofile, alone or in combination with a symptom profile, in conjunctionwith a statistical algorithm. In certain instances, the statisticalalgorithm is a learning statistical classifier system. The learningstatistical classifier system comprises any of the learning statisticalclassifier systems described above.

In certain instances, the statistical algorithm is a single learningstatistical classifier system. Preferably, the single learningstatistical classifier system is a tree-based statistical algorithm(e.g., RF, C&RT, etc.). In certain other instances, the statisticalalgorithm is a combination of at least two learning statisticalclassifier systems. Preferably, the combination of learning statisticalclassifier systems comprises a RF and NN (e.g., artificial NN, etc.),e.g., used in tandem or parallel. As a non-limiting example, a RF canfirst be used to generate a prediction or probability value based uponthe diagnostic marker profile, alone or in combination with a symptomprofile, and a NN can then be used to determine the presence or severityof IBS in the individual or IBS drug efficacy based upon the predictionor probability value and the same or different diagnostic marker profileor combination of profiles.

In some instances, the data obtained from using the learning statisticalclassifier system or systems can be processed using a processingalgorithm. Such a processing algorithm can be selected, for example,from the group consisting of a multilayer perceptron, backpropagationnetwork, and Levenberg-Marquardt algorithm. In other instances, acombination of such processing algorithms can be used, such as in aparallel or serial fashion.

In certain embodiments, the methods of the present invention can furthercomprise comparing the presence or severity of IBS in the individualdetermined in step (b) to the presence or severity of IBS in theindividual at an earlier time. As a non-limiting example, the presenceor severity of IBS determined for an individual receiving an IBS drugcan be compared to the presence or severity of IBS determined for thesame individual before initiation of use of the IBS drug or at anearlier time in therapy. In certain other embodiments, the methods ofthe present invention can comprise determining the effectiveness of theIBS drug by comparing the effectiveness of the IBS drug determined instep (b) to the effectiveness of the IBS drug in the individual at anearlier time in therapy. In additional embodiments, the methods canfurther comprise sending the IBS monitoring results to a clinician,e.g., a gastroenterologist or a general practitioner.

In a further aspect, the present invention provides a computer-readablemedium including code for controlling one or more processors to classifywhether a sample from an individual is associated with IBS, the codecomprising:

instructions to apply a statistical process to a data set comprising adiagnostic marker profile to produce a statistically derived decisionclassifying the sample as an IBS sample or non-IBS sample based upon thediagnostic marker profile,

wherein the diagnostic marker profile indicates the presence or level ofat least one diagnostic marker in the sample.

In some embodiments, the diagnostic marker profile indicates thepresence or level of at least one, two, three, four, five, six, seven,eight, nine, ten, or more diagnostic markers selected from the groupconsisting of a cytokine (e.g., IL-8, IL-1β, TWEAK, leptin, OPG, MIP-3β,GROα, CXCL4/PF-4, and/or CXCL7/NAP-2), growth factor (e.g., EGF, VEGF,PEDF, BDNF, and/or SDGF), anti-neutrophil antibody (e.g., ANCA, pANCA,cANCA, NSNA, and/or SAPPA), ASCA (e.g., ASCA-IgA, ASCA-IgG, and/orASCA-IgM), antimicrobial antibody (e.g., anti-OmpC antibody,anti-flagellin antibody, and/or anti-I2 antibody), lactoferrin, anti-tTGantibody, lipocalin (e.g., NGAL, NGAL/MMP-9 complex), MMP (e.g., MMP-9),TIMP (e.g., TIMP-1), alpha-globulin (e.g., alpha-2-macroglobulin,haptoglobin, and/or orosomucoid), actin-severing protein (e.g.,gelsolin), S100 protein (e.g., calgranulin), fibrinopeptide (e.g.,FIBA), CGRP, tachykinin (e.g., Substance P), ghrelin, neurotensin,corticotropin-releasing hormone, and combinations thereof. The presenceor level of other diagnostic markers such as, for example,anti-lactoferrin antibody, L-selectin/CD62L, elastase, C-reactiveprotein (CRP), calprotectin, anti-U1-70 kDa autoantibody, zona occludens1 (ZO-1), vasoactive intestinal peptide (VIP), serum amyloid A, and/orgastrin can also be indicative of the diagnostic marker profile.

In other embodiments, the computer-readable medium for ruling in IBScomprises instructions to apply a statistical process to a data setcomprising a diagnostic marker profile optionally in combination with asymptom profile which indicates the presence or severity of at least onesymptom in the individual to produce a statistically derived decisionclassifying the sample as an IBS sample or non-IBS sample based upon thediagnostic marker profile and the symptom profile. One skilled in theart will appreciate that the statistical process can be applied to thediagnostic marker profile and the symptom profile simultaneously orsequentially in any order.

In one embodiment, the statistical process is a learning statisticalclassifier system. Examples of learning statistical classifier systemssuitable for use in the present invention are described above. Incertain instances, the statistical process is a single learningstatistical classifier system such as, for example, a RF or C&RT. Incertain other instances, the statistical process is a combination of atleast two learning statistical classifier systems. As a non-limitingexample, the combination of learning statistical classifier systemscomprises a RF and a NN, e.g., used in tandem. In some instances, thedata obtained from using the learning statistical classifier system orsystems can be processed using a processing algorithm.

In a related aspect, the present invention provides a computer-readablemedium including code for controlling one or more processors to classifywhether a sample from an individual is associated with IBS, the codecomprising:

-   -   (a) instructions to apply a first statistical process to a data        set comprising a diagnostic marker profile to produce a        statistically derived decision classifying the sample as an IBD        sample or non-IBD sample based upon the diagnostic marker        profile, wherein the diagnostic marker profile indicates the        presence or level of at least one diagnostic marker in the        sample; and    -   if the sample is classified as a non-IBD sample,    -   (b) instructions to apply a second statistical process to the        same or different data set to produce a second statistically        derived decision classifying the non-IBD sample as an IBS sample        or non-IBS sample.

In some embodiments, the diagnostic marker profile indicates thepresence or level of at least one, two, three, four, five, six, seven,eight, nine, ten, or more diagnostic markers selected from the groupconsisting of a cytokine (e.g., IL-8, IL-1β, TWEAK, leptin, OPG, MIP-3β,GROα, CXCL4/PF-4, and/or CXCL7/NAP-2), growth factor (e.g., EGF, VEGF,PEDF, BDNF, and/or SDGF), anti-neutrophil antibody (e.g., ANCA, pANCA,cANCA, NSNA, and/or SAPPA), ASCA (e.g., ASCA-IgA, ASCA-IgG, and/orASCA-IgM), antimicrobial antibody (e.g., anti-OmpC antibody,anti-flagellin antibody, and/or anti-I2 antibody), lactoferrin, anti-tTGantibody, lipocalin (e.g., NGAL, NGAL/MMP-9 complex), MMP (e.g., MMP-9),TIMP (e.g., TIMP-1), alpha-globulin (e.g., alpha-2-macroglobulin,haptoglobin, and/or orosomucoid), actin-severing protein (e.g.,gelsolin), S100 protein (e.g., calgranulin), fibrinopeptide (e.g.,FIBA), CGRP, tachykinin (e.g., Substance P), ghrelin, neurotensin,corticotropin-releasing hormone, and combinations thereof. The presenceor level of other diagnostic markers such as, for example,anti-lactoferrin antibody, L-selectin/CD62L, elastase, C-reactiveprotein (CRP), calprotectin, anti-U1-70 kDa autoantibody, zona occludens1 (ZO-1), vasoactive intestinal peptide (VIP), serum amyloid A, and/orgastrin can also be indicative of the diagnostic marker profile.

In other embodiments, the computer-readable medium for first ruling outIBD and then ruling in IBS comprises instructions to apply a firststatistical process to a data set comprising a diagnostic marker profileoptionally in combination with a symptom profile which indicates thepresence or severity of at least one symptom in the individual toproduce a statistically derived decision classifying the sample as anIBD sample or non-IBD sample based upon the diagnostic marker profileand the symptom profile; and if the sample is classified as a non-IBDsample, instructions to apply a second statistical process to the sameor different data set to produce a second statistically derived decisionclassifying the non-IBD sample as an IBS sample or non-IBS sample. Oneskilled in the art will appreciate that the first and/or secondstatistical process can be applied to the diagnostic marker profile andthe symptom profile simultaneously or sequentially in any order.

In one embodiment, the first and second statistical processes areimplemented in different processors. Alternatively, the first and secondstatistical processes are implemented in a single processor. In anotherembodiment, the first statistical process is a learning statisticalclassifier system. Examples of learning statistical classifier systemssuitable for use in the present invention are described above. Incertain instances, the first and/or second statistical process is asingle learning statistical classifier system such as, for example, a RFor C&RT. In certain other instances, the first and/or second statisticalprocess is a combination of at least two learning statistical classifiersystems. As a non-limiting example, the combination of learningstatistical classifier systems comprises a RF and a NN or SVM, e.g.,used in tandem. In some instances, the data obtained from using thelearning statistical classifier system or systems can be processed usinga processing algorithm.

In an additional aspect, the present invention provides a system forclassifying whether a sample from an individual is associated with IBS,the system comprising:

-   -   (a) a data acquisition module configured to produce a data set        comprising a diagnostic marker profile, wherein the diagnostic        marker profile indicates the presence or level of at least one        diagnostic marker in the sample;    -   (b) a data processing module configured to process the data set        by applying a statistical process to the data set to produce a        statistically derived decision classifying the sample as an IBS        sample or non-IBS sample based upon the diagnostic marker        profile; and    -   (c) a display module configured to display the statistically        derived decision.

In some embodiments, the diagnostic marker profile indicates thepresence or level of at least one, two, three, four, five, six, seven,eight, nine, ten, or more diagnostic markers selected from the groupconsisting of a cytokine (e.g., IL-8, IL-1β, TWEAK, leptin, OPG, MIP-3β,GROα, CXCL4/PF-4, and/or CXCL7/NAP-2), growth factor (e.g., EGF, VEGF,PEDF, BDNF, and/or SDGF), anti-neutrophil antibody (e.g., ANCA, pANCA,cANCA, NSNA, and/or SAPPA), ASCA (e.g., ASCA-IgA, ASCA-IgG, and/orASCA-IgM), antimicrobial antibody (e.g., anti-OmpC antibody,anti-flagellin antibody, and/or anti-I2 antibody), lactoferrin, anti-tTGantibody, lipocalin (e.g., NGAL, NGAL/MMP-9 complex), MMP (e.g., MMP-9),TIMP (e.g., TIMP-1), alpha-globulin (e.g., alpha-2-macroglobulin,haptoglobin, and/or orosomucoid), actin-severing protein (e.g.,gelsolin), S100 protein (e.g., calgranulin), fibrinopeptide (e.g.,FIBA), CGRP, tachykinin (e.g., Substance P), ghrelin, neurotensin,corticotropin-releasing hormone, and combinations thereof. The presenceor level of other diagnostic markers such as, for example,anti-lactoferrin antibody, L-selectin/CD62L, elastase, C-reactiveprotein (CRP), calprotectin, anti-U1-70 kDa autoantibody, zona occludens1 (ZO-1), vasoactive intestinal peptide (VIP), serum amyloid A, and/orgastrin can also be indicative of the diagnostic marker profile.

In other embodiments, the system for ruling in IBS comprises a dataacquisition module configured to produce a data set comprising adiagnostic marker profile optionally in combination with a symptomprofile which indicates the presence or severity of at least one symptomin the individual; a data processing module configured to process thedata set by applying a statistical process to the data set to produce astatistically derived decision classifying the sample as an IBS sampleor non-IBS sample based upon the diagnostic marker profile and thesymptom profile; and a display module configured to display thestatistically derived decision.

In one embodiment, the statistical process is a learning statisticalclassifier system. Examples of learning statistical classifier systemssuitable for use in the present invention are described above. Incertain instances, the statistical process is a single learningstatistical classifier system such as, for example, a RF or C&RT. Incertain other instances, the statistical process is a combination of atleast two learning statistical classifier systems, e.g., used in tandemor parallel. In some embodiments, the data obtained from using thelearning statistical classifier system or systems can be processed usinga processing algorithm.

In a related aspect, the present invention provides a system forclassifying whether a sample from an individual is associated with IBS,the system comprising:

-   -   (a) a data acquisition module configured to produce a data set        comprising a diagnostic marker profile, wherein the diagnostic        marker profile indicates the presence or level of at least one        diagnostic marker in the sample;    -   (b) a data processing module configured to process the data set        by applying a first statistical process to the data set to        produce a first statistically derived decision classifying the        sample as an IBD sample or non-IBD sample based upon the        diagnostic marker profile;    -   if the sample is classified as a non-IBD sample, a data        processing module configured to apply a second statistical        process to the same or different data set to produce a second        statistically derived decision classifying the non-IBD sample as        an IBS sample or non-IBS sample; and    -   (c) a display module configured to display the first and/or the        second statistically derived decision.

In some embodiments, the diagnostic marker profile indicates thepresence or level of at least one, two, three, four, five, six, seven,eight, nine, ten, or more diagnostic markers selected from the groupconsisting of a cytokine (e.g., IL-8, IL-1β, TWEAK, leptin, OPG, MIP-3β,GROα, CXCL4/PF-4, and/or CXCL7/NAP-2), growth factor (e.g., EGF, VEGF,PEDF, BDNF, and/or SDGF), anti-neutrophil antibody (e.g., ANCA, pANCA,cANCA, NSNA, and/or SAPPA), ASCA (e.g., ASCA-IgA, ASCA-IgG, and/orASCA-IgM), antimicrobial antibody (e.g., anti-OmpC antibody,anti-flagellin antibody, and/or anti-12 antibody), lactoferrin, anti-tTGantibody, lipocalin (e.g., NGAL, NGAL/MMP-9 complex), MMP (e.g., MMP-9),TIMP (e.g., TIMP-1), alpha-globulin (e.g., alpha-2-macroglobulin,haptoglobin, and/or orosomucoid), actin-severing protein (e.g.,gelsolin), S100 protein (e.g., calgranulin), fibrinopeptide (e.g.,FIBA), CGRP, tachykinin (e.g., Substance P), ghrelin, neurotensin,corticotropin-releasing hormone, and combinations thereof. The presenceor level of other diagnostic markers such as, for example,anti-lactoferrin antibody, L-selectin/CD62L, elastase, C-reactiveprotein (CRP), calprotectin, anti-U1-70 kDa autoantibody, zona occludens1 (ZO-1), vasoactive intestinal peptide (VIP), serum amyloid A, and/orgastrin can also be indicative of the diagnostic marker profile.

In other embodiments, the system for first ruling out IBD and thenruling in IBS comprises a data acquisition module configured to producea data set comprising a diagnostic marker profile optionally incombination with a symptom profile which indicates the presence orseverity of at least one symptom in the individual; a data processingmodule configured to process the data set by applying a firststatistical process to the data set to produce a first statisticallyderived decision classifying the sample as an IBD sample or non-IBDsample based upon the diagnostic marker profile and the symptom profile;if the sample is classified as a non-IBD sample, a data processingmodule configured to apply a second statistical process to the same ordifferent data set to produce a second statistically derived decisionclassifying the non-IBD sample as an IBS sample or non-IBS sample; and adisplay module configured to display the first and/or the secondstatistically derived decision.

In one embodiment, the first and/or second statistical process is alearning statistical classifier system. Examples of learning statisticalclassifier systems suitable for use in the present invention aredescribed above. In certain instances, the first and/or secondstatistical process is a single learning statistical classifier systemsuch as, for example, a RF or C&RT. In certain other instances, thefirst and/or second statistical process is a combination of at least twolearning statistical classifier systems, e.g., used in tandem orparallel. In some instances, the data obtained from using the learningstatistical classifier system or systems can be processed using aprocessing algorithm. In another embodiment, the first and secondstatistical processes are implemented in different processors.Alternatively, the first and second statistical processes areimplemented in a single processor.

IV. Diseases and Disorders with IBS-like Symptoms

A variety of structural or metabolic diseases and disorders can causesigns or symptoms that are similar to IBS. As non-limiting examples,patients with diseases and disorders such as inflammatory bowel disease(IBD), Celiac disease (CD), acute inflammation, diverticulitis, ilealpouch-anal anastomosis, microscopic colitis, chronic infectiousdiarrhea, lactase deficiency, cancer (e.g., colorectal cancer), amechanical obstruction of the small intestine or colon, an entericinfection, ischemia, maldigestion, malabsorption, endometriosis, andunidentified inflammatory disorders of the intestinal tract can presentwith abdominal discomfort associated with mild to moderate pain and achange in the consistency and/or frequency of stools that are similar toIBS. Additional IBS-like symptoms can include chronic diarrhea orconstipation or an alternating form of each, weight loss, abdominaldistention or bloating, and mucus in the stool.

Most IBD patients can be classified into one of two distinct clinicalsubtypes, Crohn's disease and ulcerative colitis. Crohn's disease is aninflammatory disease affecting the lower part of the ileum and ofteninvolving the colon and other regions of the intestinal tract.Ulcerative colitis is characterized by an inflammation localized mostlyin the mucosa and submucosa of the large intestine. Patients sufferingfrom these clinical subtypes of IBD typically have IBS-like symptomssuch as, for example, abdominal pain, chronic diarrhea, weight loss, andcramping.

The clinical presentation of Celiac disease is also characterized byIBS-like symptoms such as abdominal discomfort associated with chronicdiarrhea, weight loss, and abdominal distension. Celiac disease is animmune-mediated disorder of the intestinal mucosa that is typicallyassociated with villous atrophy, crypt hyperplasia, and/or inflammationof the mucosal lining of the small intestine. In addition to themalabsorption of nutrients, individuals with Celiac disease are at riskfor mineral deficiency, vitamin deficiency, osteoporosis, autoimmunediseases, and intestinal malignancies (e.g., lymphoma and carcinoma). Itis thought that exposure to proteins such as gluten (e.g., glutenin andprolamine proteins which are present in wheat, rye, barley, oats,millet, triticale, spelt, and kamut), in the appropriate genetic andenvironmental context, is responsible for causing Celiac disease.

Other diseases and disorders characterized by intestinal inflammationthat present with IBS-like symptoms include, for example, acuteinflammation, diverticulitis, ileal pouch-anal anastomosis, microscopiccolitis, and chronic infectious diarrhea, as well as unidentifiedinflammatory disorders of the intestinal tract. Patients experiencingepisodes of acute inflammation typically have elevated C-reactiveprotein (CRP) levels in addition to IBS-like symptoms. CRP is producedby the liver during the acute phase of the inflammatory process and isusually released about 24 hours post-commencement of the inflammatoryprocess. Patients suffering from diverticulitis, ileal pouch-analanastomosis, microscopic colitis, and chronic infectious diarrheatypically have elevated fecal lactoferrin and/or calprotectin levels inaddition to IBS-like symptoms. Lactoferrin is a glycoprotein secreted bymucosal membranes and is the major protein in the secondary granules ofleukocytes. Leukocytes are commonly recruited to inflammatory siteswhere they are activated, releasing granule content to the surroundingarea. This process increases the concentration of lactoferrin in thestool.

Increased lactoferrin levels are observed in patients with ilealpouch-anal anastomosis (i.e., a pouch is created following completeresection of colon in severe cases of Crohn's disease) when compared toother non-inflammatory conditions of the pouch, like irritable pouchsyndrome. Elevated levels of lactoferrin are also observed in patientswith diverticulitis, a condition in which bulging pouches (i.e.,diverticula) in the digestive tract become inflamed and/or infected,causing severe abdominal pain, fever, nausea, and a marked change inbowel habits. Microscopic colitis is a chronic inflammatory disorderthat is also associated with increased fecal lactoferrin levels.Microscopic colitis is characterized by persistent watery diarrhea(non-bloody), abdominal pain usually associated with weight loss, anormal mucosa during colonoscopy and radiological examination, and veryspecific histopathological changes. Microscopic colitis consists of twodiseases, collagenous colitis and lymphocytic colitis. Collagenouscolitis is of unknown etiology and is found in patients with long-termwatery diarrhea and a normal colonoscopy examination. Both collagenouscolitis and lymphocytic colitis are characterized by increasedlymphocytes in the lining of the colon. Collagenous colitis is furthercharacterized by a thickening of the sub-epithelial collagen layer ofthe colon. Chronic infectious diarrhea is an illness that is alsoassociated with increased fecal lactoferrin levels. Chronic infectiousdiarrhea is usually caused by a bacterial, viral, or protozoaninfection, with patients presenting with IBS-like symptoms such asdiarrhea and abdominal pain. Increased lactoferrin levels are alsoobserved in patients with IBD.

In addition to determining CRP and/or lactoferrin and/or calprotectinlevels, diseases and disorders associated with intestinal inflammationcan also be ruled out by detecting the presence of blood in the stool,such as fecal hemoglobin. Intestinal bleeding that occurs without thepatient's knowledge is called occult or hidden bleeding. The presence ofoccult bleeding (e.g., fecal hemoglobin) is typically observed in astool sample from the patient. Other conditions such as ulcers (e.g.,gastric, duodenal), cancer (e.g., stomach cancer, colorectal cancer),and hemorrhoids can also present with IBS-like symptoms includingabdominal pain and a change in the consistency and/or frequency ofstools.

In addition, fecal calprotectin levels can also be assessed.Calprotectin is a calcium binding protein with antimicrobial activityderived predominantly from neutrophils and monocytes. Calprotectin hasbeen found to have clinical relevance in cystic fibrosis, rheumatoidarthritis, IBD, colorectal cancer, HIV, and other inflammatory diseases.Its level has been measured in serum, plasma, oral, cerebrospinal andsynovial fluids, urine, and feces. Advantages of fecal calprotectin inGI disorders have been recognized: stable for 3-7 days at roomtemperature enabling sample shipping through regular mail; correlated tofecal alpha 1-antitrypsin in patients with Crohn's disease; and elevatedin a great majority of patients with gastrointestinal carcinomas andIBD. It was found that fecal calprotectin correlates well withendoscopic and histological gradings of disease activity in ulcerativecolitis, and with fecal excretion of indium-11-labelled neutrophilicgranulocytes, which is a standard of disease activity in IBD.

In view of the foregoing, it is clear that a wide array of diseases anddisorders can cause IBS-like symptoms, thereby creating a substantialobstacle for definitively classifying a sample as an IBS sample.However, the present invention overcomes this limitation by classifyinga sample from an individual as an IBS sample using, for example, astatistical algorithm, or by excluding (i.e., ruling out) those diseasesand disorders that share a similar clinical presentation as IBS andidentifying (i.e., ruling in) IBS in a sample using, for example, acombination of statistical algorithms.

V. Diagnostic Markers

A variety of diagnostic markers are suitable for use in the methods,systems, and code of the present invention for classifying a sample froman individual as an IBS sample or for ruling out one or more diseases ordisorders associated with IBS-like symptoms in a sample from anindividual. Examples of diagnostic markers include, without limitation,cytokines, growth factors, anti-neutrophil antibodies,anti-Saccharomyces cerevisiae antibodies, antimicrobial antibodies,anti-tissue transglutaminase (tTG) antibodies, lipocalins, matrixmetalloproteinases (MMPs), complexes of lipocalin and MMP, tissueinhibitor of metalloproteinases (TIMPs), globulins (e.g.,alpha-globulins), actin-severing proteins, S100 proteins,fibrinopeptides, calcitonin gene-related peptide (CGRP), tachykinins,ghrelin, neurotensin, corticotropin-releasing hormone (CRH), elastase,C-reactive protein (CRP), lactoferrin, anti-lactoferrin antibodies,calprotectin, hemoglobin, NOD2/CARD15, serotonin reuptake transporter(SERT), tryptophan hydroxylase-1, 5-hydroxytryptamine (5-HT), lactulose,and combinations thereof. Additional diagnostic markers for predictingIBS in accordance with the present invention can be selected using thetechniques described in Example 14. One skilled in the art will alsoknow of other diagnostic markers suitable for use in the presentinvention.

A. Cytokines

The determination of the presence or level of at least one cytokine in asample is particularly useful in the present invention. As used herein,the term “cytokine” includes any of a variety of polypeptides orproteins secreted by immune cells that regulate a range of immune systemfunctions and encompasses small cytokines such as chemokines. The term“cytokine” also includes adipocytokines, which comprise a group ofcytokines secreted by adipocytes that function, for example, in theregulation of body weight, hematopoiesis, angiogenesis, wound healing,insulin resistance, the immune response, and the inflammatory response.

In certain aspects, the presence or level of at least one cytokineincluding, but not limited to, TNF-α, TNF-related weak inducer ofapoptosis (TWEAK), osteoprotegerin (OPG), IFN-α, IFN-β, IFN-γ, IL-1α,IL-1β, IL-1 receptor antagonist (IL-1ra), IL-2, IL-4, IL-5, IL-6,soluble IL-6 receptor (sIL-6R), IL-7, IL-8, IL-9, IL-10, IL-12, IL-13,IL-15, IL-17, IL-23, and IL-27 is determined in a sample. In certainother aspects, the presence or level of at least one chemokine such as,for example, CXCL1/GRO1/GROα, CXCL2/GRO2, CXCL3/GRO3, CXCL4/PF-4,CXCL5/ENA-78, CXCL6/GCP-2, CXCL7/NAP-2, CXCL9/MIG, CXCL10/IP-10,CXCL11/I-TAC, CXCL12/SDF-1, CXCL13/BCA-1, CXCL14/BRAK, CXCL15, CXCL16,CXCL17/DMC, CCL1, CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β, CCL5/RANTES,CCL6/C10, CCL7/MCP-3, CCL8/MCP-2, CCL9/CCL10, CCL11/Eotaxin,CCL12/MCP-5, CCL13/MCP-4, CCL14/HCC-1, CCL15/MIP-5, CCL16/LEC,CCL17/TARC, CCL18/MIP-4, CCL19/MIP-3β, CCL20/MIP-3α, CCL21/SLC,CCL22/MDC, CCL23/MPIF1, CCL24/Eotaxin-2, CCL25/TECK, CCL26/Eotaxin-3,CCL27/CTACK, CCL28/MEC, CL1, CL2, and CX₃CL1 is determined in a sample.In certain further aspects, the presence or level of at least oneadipocytokine including, but not limited to, leptin, adiponectin,resistin, active or total plasminogen activator inhibitor-1 (PAI-1),visfatin, and retinol binding protein 4 (RBP4) is determined in asample. Preferably, the presence or level of IL-8, IL-1β, TWEAK, leptin,OPG, MIP-3β, GROα, CXCL4/PF-4, and/or CXCL7/NAP-2 is determined.

In certain instances, the presence or level of a particular cytokine isdetected at the level of mRNA expression with an assay such as, forexample, a hybridization assay or an amplification-based assay. Incertain other instances, the presence or level of a particular cytokineis detected at the level of protein expression using, for example, animmunoassay (e.g., ELISA) or an immunohistochemical assay. SuitableELISA kits for determining the presence or level of a cytokine such asIL-8, IL-1β, MIP-3β, GROα, CXCL4/PF-4, or CXCL7/NAP-2 in a serum,plasma, saliva, or urine sample are available from, e.g., R&D Systems,Inc. (Minneapolis, Minn.), Neogen Corp. (Lexington, Ky.), AlpcoDiagnostics (Salem, N.H.), Assay Designs, Inc. (Ann Arbor, Mich.), BDBiosciences Pharmingen (San Diego, Calif.), Invitrogen (Camarillo,Calif.), Calbiochem (San Diego, Calif.), CHEMICON International, Inc.(Temecula, Calif.), Antigenix America Inc. (Huntington Station, N.Y.),QIAGEN Inc. (Valencia, Calif.), Bio-Rad Laboratories, Inc. (Hercules,Calif.), and/or Bender MedSystems Inc. (Burlingame, Calif.).

1. TWEAK

TWEAK is a member of the TNF superfamily of structurally relatedcytokines. Full-length, membrane-anchored TWEAK can be found on thesurface of many cell types and a smaller, biologically active form,generated via proteolytic processing, has also been detected in theextracellular milieu (see, e.g., Chicheportiche et al., J. Biol. Chem.,272:32401-32410 (1997)). TWEAK acts via binding to a TNF receptorsuperfamily member named fibroblast growth factor-inducible 14 (Fn14;also known as tumor necrosis factor receptor superfamily member 12A orTNFRSF12A). TWEAK has multiple biological activities, includingstimulation of cell growth and angiogenesis, induction of inflammatorycytokines, and stimulation of apoptosis (see, e.g., Wiley et al.,Cytokine Growth Factor Rev., 14:241-249 (2003)). In particular, TWEAKhas been shown to induce the expression of PGE2, MMP-1, IL-6, IL-8,RANTES, and IP-10 in fibroblasts and synoviocytes, and to upregulateICAM-1, E-selectin, IL-8, and MCP-1 expression in endothelial cells(see, e.g., Campbell et al., Front. Biosci., 9:2273-2284 (2004)). It hasalso been demonstrated that TWEAK binding to the Fn14 receptor, orconstitutive Fn14 overexpression, activates the NF-κB signaling pathway,which plays an important role in immune and inflammatory processes,oncogenesis, cancer therapy resistance, and tumorigenesis (see, e.g.,Winkles et al., Cancer Lett., 235:11-17 (2006); and Winkles et al.,Front. Biosci., 12:2761-2771 (2007)). One skilled in the art willappreciate that TWEAK is also known as tumor necrosis factor ligandsuperfamily member 12 (TNFSF12), APO3 ligand (APO3L), CD255, DR3 ligand,FN14, and UNQ181/PRO207.

Suitable ELISA kits for determining the presence or level of TWEAK in abiological sample such as a serum, plasma, saliva, or urine sample areavailable from, e.g., Antigenix America Inc. (Huntington Station, N.Y.),Bender MedSystems Inc. (Burlingame, Calif.), Agdia Inc. (Elkhart, Ind.),American Research Products Inc. (Belmont, Mass.), Biomeda Corp. (FosterCity, Calif.), BioVision, Inc. (Mountain View, Calif.), and KamiyaBiomedical Co. (Seattle, Wash.).

2. Osteoprotegerin (OPG)

OPG is a 401-amino acid member of the TNF superfamily of structurallyrelated cytokines. OPG, which is homologous to the receptor activator ofNFκB (RANK), inhibits the differentiation of macrophages intoosteoclasts and regulates the resorption of osteoclasts by acting as asoluble decoy receptor for RANK ligand (RANKL; also known as OPG ligand(OPGL)). As a result, the OPG-RANK-RANKL system plays a direct andessential role in the formation, function, and survival of osteoclasts.The OPG-RANK-RANKL system has also been shown to modulate cancer cellmigration, thus controlling the development of bone metastases. Oneskilled in the art will appreciate that OPG is also known asosteoprotegrin and osteoclastogenesis inhibitory factor (OCIF).

Suitable ELISA kits for determining the presence or level of OPG in aserum, plasma, saliva, or urine sample are available from, e.g.,Antigenix America Inc. (Huntington Station, N.Y.), ImmunodiagnosticSystems Ltd. (Boldon, United Kingdom), and BioVendor, LLC (Candler,N.C.).

3. Leptin

Leptin, a member of the adipocytokine family of cytokines, is a 16-kDpeptide hormone that plays a critical role in the regulation of bodyweight by inhibiting food intake and stimulating energy expenditure. Itis predominantly synthesized by adipocytes and circulates in the plasmain amounts proportional to body fat content (see, e.g., Maffei et al.,Nat. Med., 1:1155-1161 (1995); Considine et al., Diabetes, 45:992-994(1996)). Leptin displays a high degree of homology among differentspecies and it is also analogous in structure to other cytokines (see,e.g., Madej et al., FEBS Lett., 373:13-18 (1995)). Leptin acts throughthe leptin receptor, a single-transmembrane-domain receptor of the classI cytokine superfamily of receptors, which are characterized byextracellular motifs of four cysteine residues and a number offibronectin type III domains (see, e.g., Heim, Eur. J Clin. Invest.,26:1-12 (1996)). The leptin receptor is known to exist as a homodimerand is activated by conformational changes that occur following ligandbinding to the receptor (see, e.g., Devos et al., J. Biol. Chem.,272:18304-18310 (1997)). Six leptin receptor isoforms, generated byalternate slicing, have been identified to date (see, e.g., Wang et al.,Nature, 393:684-688 (1998); Lee et al., Nature, 379:632-635 (1996)).

Suitable ELISA kits for determining the presence or level of leptin in abiological sample such as a serum, plasma, saliva, or urine sample areavailable from, e.g., R&D Systems, Inc. (Minneapolis, Minn.), B-BridgeInternational (Mountain View, Calif.), Neogen Corp. (Lexington, Ky.),Assay Designs, Inc. (Ann Arbor, Mich.), Invitrogen (Camarillo, Calif.),CHEMICON International, Inc. (Temecula, Calif.), Antigenix America Inc.(Huntington Station, N.Y.), LINCO Research, Inc. (St. Charles, Mo.),Diagnostic Systems Laboratories, Inc. (Webster, Tex.), Immuno-BiologicalLaboratories, Inc. (Minneapolis, Minn.), and Cayman Chemical Co. (AnnArbor, Mich.).

B. Growth Factors

The determination of the presence or level of one or more growth factorsin a sample is also useful in the present invention. As used herein, theterm “growth factor” includes any of a variety of peptides,polypeptides, or proteins that are capable of stimulating cellularproliferation and/or cellular differentiation.

In certain aspects, the presence or level of at least one growth factorincluding, but not limited to, epidermal growth factor (EGF),heparin-binding epidermal growth factor (HB-EGF), vascular endothelialgrowth factor (VEGF), pigment epithelium-derived factor (PEDF; alsoknown as SERPINF1), amphiregulin (AREG; also known as schwannoma-derivedgrowth factor (SDGF)), basic fibroblast growth factor (bFGF), hepatocytegrowth factor (HGF), transforming growth factor-α (TGF-α), transforminggrowth factor-β (TGF-β), bone morphogenetic proteins (e.g., BMP1-BMP15),platelet-derived growth factor (PDGF), nerve growth factor (NGF),β-nerve growth factor (β-NGF), neurotrophic factors (e.g., brain-derivedneurotrophic factor (BDNF), neurotrophin 3 (NT3), neurotrophin 4 (NT4),etc.), growth differentiation factor-9 (GDF-9), granulocyte-colonystimulating factor (G-CSF), granulocyte-macrophage colony stimulatingfactor (GM-CSF), myostatin (GDF-8), erythropoietin (EPO), andthrombopoietin (TPO) is determined in a sample. Preferably, the presenceor level of EGF, VEGF, PEDF, amphiregulin (SDGF), and/or BDNF isdetermined.

In certain instances, the presence or level of a particular growthfactor is detected at the level of mRNA expression with an assay suchas, for example, a hybridization assay or an amplification-based assay.In certain other instances, the presence or level of a particular growthfactor is detected at the level of protein expression using, forexample, an immunoassay (e.g., ELISA) or an immunohistochemical assay.Suitable ELISA kits for determining the presence or level of a growthfactor such as EGF, VEGF, PEDF, SDGF, or BDNF in a serum, plasma,saliva, or urine sample are available from, e.g., Antigenix America Inc.(Huntington Station, N.Y.), Promega (Madison, Wis.), R&D Systems, Inc.(Minneapolis, Minn.), Invitrogen (Camarillo, Calif.), CHEMICONInternational, Inc. (Temecula, Calif.), Neogen Corp. (Lexington, Ky.),PeproTech (Rocky Hill, N.J.), Alpco Diagnostics (Salem, N.H.), PierceBiotechnology, Inc. (Rockford, Ill.), and/or Abazyme (Needham, Mass.).

C. Lipocalins

The determination of the presence or level of one or more lipocalins ina sample is also useful in the present invention. As used herein, theterm “lipocalin” includes any of a variety of small extracellularproteins that are characterized by several common molecular recognitionproperties: the ability to bind a range of small hydrophobic molecules;binding to specific cell-surface receptors; and the formation ofcomplexes with soluble macromolecules (see, e.g., Flowers, Biochem. J.,318:1-14 (1996)). The varied biological functions of lipocalins aremediated by one or more of these properties. The lipocalin proteinfamily exhibits great functional diversity, with roles in retinoltransport, invertebrate cryptic coloration, olfaction and pheromonetransport, and prostaglandin synthesis. Lipocalins have also beenimplicated in the regulation of cell homoeostasis and the modulation ofthe immune response, and, as carrier proteins, to act in the generalclearance of endogenous and exogenous compounds. Although lipocalinshave great diversity at the sequence level, their three-dimensionalstructure is a unifying characteristic. Lipocalin crystal structures arehighly conserved and comprise a single eight-stranded continuouslyhydrogen-bonded antiparallel beta-barrel, which encloses an internalligand-binding site.

In certain aspects, the presence or level of at least one lipocalinincluding, but not limited to, neutrophil gelatinase-associatedlipocalin (NGAL; also known as human neutrophil lipocalin (HNL) orlipocalin-2), von Ebner's gland protein (VEGP; also known aslipocalin-1), retinol-binding protein (RBP), purpurin (PURP), retinoicacid-binding protein (RABP), α_(2u)-globulin (A2U), major urinaryprotein (MUP), bilin-binding protein (BBP), α-crustacyanin, pregnancyprotein 14 (PP14), β-lactoglobulin (B1g), α₁-microglobulin (A1M), thegamma chain of C8 (C8γ), Apolipoprotein D (ApoD), lazarillo (LAZ),prostaglandin D2 synthase (PGDS), quiescence-specific protein (QSP),choroid plexus protein, odorant-binding protein (OBP), α₁-acidglycoprotein (AGP), probasin (PBAS), aphrodisin, orosomucoid, andprogestagen-associated endometrial protein (PAEP) is determined in asample. In certain other aspects, the presence or level of at least onelipocalin complex including, for example, a complex of NGAL and a matrixmetalloproteinase (e.g., NGAL/MMP-9 complex) is determined. Preferably,the presence or level of NGAL or a complex thereof with MMP-9 isdetermined.

In certain instances, the presence or level of a particular lipocalin isdetected at the level of mRNA expression with an assay such as, forexample, a hybridization assay or an amplification-based assay. Incertain other instances, the presence or level of a particular lipocalinis detected at the level of protein expression using, for example, animmunoassay (e.g., ELISA) or an immunohistochemical assay. SuitableELISA kits for determining the presence or level of a lipocalin such asNGAL in a serum, plasma, or urine sample are available from, e.g.,AntibodyShop A/S (Gentofte, Denmark), LabClinics SA (Barcelona, Spain),Lucerna-Chem AG (Luzern, Switzerland), R&D Systems, Inc. (Minneapolis,Minn.), and Assay Designs, Inc. (Ann Arbor, Mich.). Suitable ELISA kitsfor determining the presence or level of the NGAL/MMP-9 complex areavailable from, e.g., R&D Systems, Inc. (Minneapolis, Minn.). AdditionalNGAL and NGAL/MMP-9 complex ELISA techniques are described in, e.g.,Kjeldsen et al., Blood, 83:799-807 (1994); and Kjeldsen et al., J.Immunol. Methods, 198:155-164 (1996).

D. Matrix Metalloproteinases

The determination of the presence or level of at least one matrixmetalloproteinase (MMP) in a sample is also useful in the presentinvention. As used herein, the term “matrix metalloproteinase” or “MMP”includes zinc-dependent endopeptidases capable of degrading a variety ofextracellular matrix proteins, cleaving cell surface receptors,releasing apoptotic ligands, and/or regulating chemokines. MMPs are alsothought to play a major role in cell behaviors such as cellproliferation, migration (adhesion/dispersion), differentiation,angiogenesis, and host defense.

In certain aspects, the presence or level of at least one at least oneMMP including, but not limited to, MMP-1 (interstitial collagenase),MMP-2 (gelatinase-A), MMP-3 (stromelysin-1), MMP-7 (matrilysin), MMP-8(neutrophil collagenase), MMP-9 (gelatinase-B), MMP-10 (stromelysin-2),MMP-11 (stromelysin-3), MMP-12 (macrophage metalloelastase), MMP-13(collagenase-3), MMP-14, MMP-15, MMP-16, MMP-17, MMP-18 (collagenase-4),MMP-19, MMP-20 (enamelysin), MMP-21, MMP-23, MMP-24, MMP-25, MMP-26(matrilysin-2), MMP-27, and MMP-28 (epilysin) is determined in a sample.Preferably, the presence or level of MMP-9 is determined.

In certain instances, the presence or level of a particular MMP isdetected at the level of mRNA expression with an assay such as, forexample, a hybridization assay or an amplification-based assay. Incertain other instances, the presence or level of a particular MMP isdetected at the level of protein expression using, for example, animmunoassay (e.g., ELISA) or an immunohistochemical assay. SuitableELISA kits for determining the presence or level of an MMP such as MMP-9in a serum or plasma sample are available from, e.g., Calbiochem (SanDiego, Calif.), CHEMICON International, Inc. (Temecula, Calif.), and R&DSystems, Inc. (Minneapolis, Minn.).

E. Tissue Inhibitor of Metalloproteinases

The determination of the presence or level of at least one tissueinhibitor of metalloproteinase (TIMP) in a sample is also useful in thepresent invention. As used herein, the term “tissue inhibitor ofmetalloproteinase” or “TIMP” includes proteins capable of inhibitingMMPs.

In certain aspects, the presence or level of at least one at least oneTIMP including, but not limited to, TIMP-1, TIMP-2, TIMP-3,and TIMP-4 isdetermined in a sample. Preferably, the presence or level of TIMP-1 isdetermined.

In certain instances, the presence or level of a particular TIMP isdetected at the level of mRNA expression with an assay such as, forexample, a hybridization assay or an amplification-based assay. Incertain other instances, the presence or level of a particular TIMP isdetected at the level of protein expression using, for example, animmunoassay (e.g., ELISA) or an immunohistochemical assay. SuitableELISA kits for determining the presence or level of a TIMP such asTIMP-1 in a serum or plasma sample are available from, e.g., AlpcoDiagnostics (Salem, N.H.), Calbiochem (San Diego, Calif.), Invitrogen(Camarillo, Calif.), CHEMICON International, Inc. (Temecula, Calif.),and R&D Systems, Inc. (Minneapolis, Minn.).

F. Globulins

The determination of the presence or level of at least one globulin in asample is also useful in the present invention. As used herein, the term“globulin” includes any member of a heterogeneous series of families ofserum proteins which migrate less than albumin during serumelectrophoresis. Protein electrophoresis is typically used to categorizeglobulins into the following three categories: alpha-globulins (i.e.,alpha-1-globulins or alpha-2-globulins); beta-globulins; andgamma-globulins.

Alpha-globulins comprise a group of globular proteins in plasma whichare highly mobile in alkaline or electrically-charged solutions. Theygenerally function to inhibit certain blood protease and inhibitoractivity. Examples of alpha-globulins include, but are not limited to,alpha-2-macroglobulin (α2-MG), haptoglobin (Hp), orosomucoid,alpha-1-antitrypsin, alpha-1-antichymotrypsin, alpha-2-antiplasmin,antithrombin, ceruloplasmin, heparin cofactor II, retinol bindingprotein, and transcortin. Preferably, the presence or level of α2-MG,haptoglobin, and/or orosomucoid is determined. In certain instances, oneor more haptoglobin allotypes such as, for example, Hp precursor, Hpβ,Hpα1, and Hpα2, are determined.

In certain instances, the presence or level of a particular globulin isdetected at the level of mRNA expression with an assay such as, forexample, a hybridization assay or an amplification-based assay. Incertain other instances, the presence or level of a particular globulinis detected at the level of protein expression using, for example, animmunoassay (e.g., ELISA) or an immunohistochemical assay. SuitableELISA kits for determining the presence or level of a globulin such asα2-MG, haptoglobin, or orosomucoid in a serum, plasma, or urine sampleare available from, e.g., GenWay Biotech, Inc. (San Diego, Calif.)and/or Immundiagnostik AG (Bensheim, Germany)

G. Actin-Severing Proteins

The determination of the presence or level of at least oneactin-severing protein in a sample is also useful in the presentinvention. As used herein, the term “actin-severing protein” includesany member of a family of proteins involved in actin remodeling andregulation of cell motility. Non-limiting examples of actin-severingproteins include gelsolin (also known as brevin or actin-depolymerizingfactor), villin, fragmin, and adseverin. For example, gelsolin is aprotein of leukocytes, platelets, and other cells which severs actinfilaments in the presence of submicromolar calcium, thereby solatingcytoplasmic actin gels.

In certain instances, the presence or level of a particularactin-severing protein is detected at the level of mRNA expression withan assay such as, for example, a hybridization assay or anamplification-based assay. In certain other instances, the presence orlevel of a particular actin-severing protein is detected at the level ofprotein expression using, for example, an immunoassay (e.g., ELISA) oran immunohistochemical assay. Suitable ELISA techniques for determiningthe presence or level of an actin-severing protein such as gelsolin in aplasma sample are described in, e.g., Smith et al., J. Lab. Clin. Med.,110:189-195 (1987); and Hiyoshi et al., Biochem. Mol. Biol. Int.,32:755-762 (1994).

H. S100 Proteins

The determination of the presence or level of at least one S100 proteinin a sample is also useful in the present invention. As used herein, theterm “S100 protein” includes any member of a family of low molecularmass acidic proteins characterized by cell-type-specific expression andthe presence of 2 EF-hand calcium-binding domains. There are at least 21different types of S100 proteins in humans. The name is derived from thefact that S100 proteins are 100% soluble in ammonium sulfate at neutralpH. Most S100 proteins are homodimeric, consisting of two identicalpolypeptides held together by non-covalent bonds. Although S100 proteinsare structurally similar to calmodulin, they differ in that they arecell-specific, expressed in particular cells at different levelsdepending on environmental factors. S-100 proteins are normally presentin cells derived from the neural crest (e.g., Schwann cells,melanocytes, glial cells), chondrocytes, adipocytes, myoepithelialcells, macrophages, Langerhans cells, dendritic cells, andkeratinocytes. S100 proteins have been implicated in a variety ofintracellular and extracellular functions such as the regulation ofprotein phosphorylation, transcription factors, Ca²⁺ homeostasis, thedynamics of cytoskeleton constituents, enzyme activities, cell growthand differentiation, and the inflammatory response.

Calgranulin is an S100 protein that is expressed in multiple cell types,including renal epithelial cells and neutrophils, and are abundant ininfiltrating monocytes and granulocytes under conditions of chronicinflammation. Examples of calgranulins include, without limitation,calgranulin A (also known as S100A8 or MRP-8), calgranulin B (also knownas S100A9 or MRP-14), and calgranulin C (also known as S100A12).

In certain instances, the presence or level of a particular S100 proteinis detected at the level of mRNA expression with an assay such as, forexample, a hybridization assay or an amplification-based assay. Incertain other instances, the presence or level of a particular S100protein is detected at the level of protein expression using, forexample, an immunoassay (e.g., ELISA) or an immunohistochemical assay.Suitable ELISA kits for determining the presence or level of an S100protein such as calgranulin A (S100A8) or calgranulin B (S100A9) in aserum, plasma, or urine sample are available from, e.g., PeninsulaLaboratories Inc. (San Carlos, Calif.) and Hycult biotechnology b.v.(Uden, The Netherlands).

Calprotectin, the complex of S100A8 and S100A9, is a calcium- andzinc-binding protein in the cytosol of neutrophils, monocytes, andkeratinocytes. Calprotectin is a major protein in neutrophilicgranulocytes and macrophages and accounts for as much as 60% of thetotal protein in the cytosol fraction in these cells. It is therefore asurrogate marker of neutrophil turnover. Its concentration in stoolcorrelates with the intensity of neutrophil infiltration of theintestinal mucosa and with the severity of inflammation. In someinstances, calprotectin can be measured with an ELISA using small(50-100 mg) fecal samples (see, e.g., Johne et al., Scand JGastroenterol., 36:291-296 (2001)).

I. Tachykinins

The determination of the presence or level of at least one tachykinin ina sample is also useful in the present invention. As used herein, theterm “tachykinin” includes amidated neuropeptides that share thecarboxy-terminal sequence Phe-X-Gly-Leu-Met-NH₂. Tachykinins typicallybind to one or more tachykinin receptors (e.g., TACR1, TACR2, and/orTACR3).

In certain aspects, the presence or level of at least one tachykininincluding, but not limited to, substance P, neurokinin A, and neurokininB is determined in a sample. Preferably, the presence or level ofsubstance P is determined. Substance P is a peptide of 11 amino acids inlength that is released by nerve endings in both the central andperipheral nervous systems. Among the numerous biological sitesinnervated by substance P-releasing neurons are the skin, intestines,stomach, bladder, and cardiovascular system.

In certain instances, the presence or level of a particular tachykininis detected at the level of mRNA expression with an assay such as, forexample, a hybridization assay or an amplification-based assay. Incertain other instances, the presence or level of a particulartachykinin is detected at the level of protein expression using, forexample, an immunoassay (e.g., ELISA) or an immunohistochemical assay.Suitable ELISA kits for determining the presence or level of atachykinin such as substance P in a serum, plasma, saliva, or urinesample are available from, e.g., MD Biosciences Inc. (St. Paul, Minn.),Assay Designs, Inc. (Ann Arbor, Mich.), R&D Systems, Inc. (Minneapolis,Minn.), Sigma-Aldrich Corp. (St. Louis, Mo.), and Cayman Chemical Co.(Ann Arbor, Mich.).

J. Ghrelin

The determination of the presence or level of ghrelin in a sample isalso useful in the present invention. As used herein, the term “ghrelin”includes a peptide of 28 amino acids that is an endogenous ligand forthe growth hormone secretagogue receptor (GHSR) and is involved inregulating growth hormone release. Ghrelin can be acylated, typicallywith an n-octanoyl group at serine residue three, to form activeghrelin. Alternatively, ghrelin can exist as an unacylated form (i.e.,desacyl-ghrelin). Ghrelin is primarily expressed in specializedenterochromaffin cells located mainly in the mucosa of the fundus of thestomach and has metabolic effects opposite to those of leptin. Ghrelinstimulates food intake, enhances the use of carbohydrates and reducesfat utilization, increases gastric motility and acid secretion, andreduces locomotor activity.

In certain instances, the presence or level of ghrelin is detected atthe level of mRNA expression with an assay such as, for example, ahybridization assay or an amplification-based assay. In certain otherinstances, the presence or level of ghrelin is detected at the level ofprotein expression using, for example, an immunoassay (e.g., ELISA) oran immunohistochemical assay. Suitable ELISA kits for determining thepresence or level of active ghrelin or desacyl-ghrelin in a serum,plasma, saliva, or urine sample are available from, e.g., AlpcoDiagnostics (Salem, N.H.), Cayman Chemical Co. (Ann Arbor, Mich.), LINCOResearch, Inc. (St. Charles, Mo.), and Diagnostic Systems Laboratories,Inc. (Webster, Tex.).

K. Neurotensin

The determination of the presence or level of neurotensin in a sample isalso useful in the present invention. As used herein, the term“neurotensin” includes a tridecapeptide that is widely distributedthroughout the central nervous system and the gastrointestinal tract.Neurotensin has been identified as an important mediator in thedevelopment and progression of several gastrointestinal functions anddisease conditions, exerting its effects by interacting with specificreceptors that act directly or indirectly on nerves, epithelial cells,and/or cells of the immune and inflammatory systems (see, e.g., Zhao etal., Peptides, 27:2434-2444 (2006)).

In certain instances, the presence or level of neurotensin is detectedat the level of mRNA expression with an assay such as, for example, ahybridization assay or an amplification-based assay. In certain otherinstances, the presence or level of neurotensin is detected at the levelof protein expression using, for example, an immunoassay (e.g., ELISA)or an immunohistochemical assay. Suitable ELISA techniques fordetermining the presence or level of neurotensin in a sample aredescribed in, e.g., Davis et al., J. Neurosci. Methods, 14:15-23 (1985);and Williams et al., J. Histochem. Cytochem., 37:831-841 (1989).

L. Corticotropin-Releasing Hormone

The determination of the presence or level of corticotropin-releasinghormone (CRH; also known as corticotropin-releasing factor or CRF) in asample is also useful in the present invention. As used herein, the term“corticotropin-releasing hormone,” “CRH,” “corticotropin-releasingfactor,” or “CRF” includes a 41-amino acid peptide secreted by theparaventricular nucleus of the hypothalamus that mediates the proximalpart of the response to stress in mammals such as humans. CRH typicallybinds to one or more corticotropin-releasing hormone receptors (e.g.,CRHR1 and/or CRHR2). CRH is expressed by the hypothalamus, spinal cord,stomach, spleen, duodenum, adrenal gland, and placenta.

In certain instances, the presence or level of CRH is detected at thelevel of mRNA expression with an assay such as, for example, ahybridization assay or an amplification-based assay. In certain otherinstances, the presence or level of CRH is detected at the level ofprotein expression using, for example, an immunoassay (e.g., ELISA) oran immunohistochemical assay. Suitable ELISA kits for determining thepresence or level of CRH in a serum, plasma, saliva, or urine sample areavailable from, e.g., Alpco Diagnostics (Salem, N.H.) and Cosmo Bio Co.,Ltd. (Tokyo, Japan).

M. Anti-Neutrophil Antibodies

The determination of ANCA levels and/or the presence or absence of pANCAin a sample is also useful in the present invention. As used herein, theterm “anti-neutrophil cytoplasmic antibody” or “ANCA” includesantibodies directed to cytoplasmic and/or nuclear components ofneutrophils. ANCA activity can be divided into several broad categoriesbased upon the ANCA staining pattern in neutrophils: (1) cytoplasmicneutrophil staining without perinuclear highlighting (cANCA); (2)perinuclear staining around the outside edge of the nucleus (pANCA); (3)perinuclear staining around the inside edge of the nucleus (NSNA); and(4) diffuse staining with speckling across the entire neutrophil(SAPPA). In certain instances, pANCA staining is sensitive to DNasetreatment. The term ANCA encompasses all varieties of anti-neutrophilreactivity, including, but not limited to, cANCA, pANCA, NSNA, andSAPPA. Similarly, the term ANCA encompasses all immunoglobulin isotypesincluding, without limitation, immunoglobulin A and G.

ANCA levels in a sample from an individual can be determined, forexample, using an immunoassay such as an enzyme-linked immunosorbentassay (ELISA) with alcohol-fixed neutrophils. The presence or absence ofa particular category of ANCA such as pANCA can be determined, forexample, using an immunohistochemical assay such as an indirectfluorescent antibody (IFA) assay. Preferably, the presence or absence ofpANCA in a sample is determined using an immunofluorescence assay withDNase-treated, fixed neutrophils. In addition to fixed neutrophils,antigens specific for ANCA that are suitable for determining ANCA levelsinclude, without limitation, unpurified or partially purified neutrophilextracts; purified proteins, protein fragments, or synthetic peptidessuch as histone H1 or ANCA-reactive fragments thereof (see, e.g., U.S.Pat. No. 6,074,835); histone H1-like antigens, porin antigens,Bacteroides antigens, or ANCA-reactive fragments thereof (see, e.g.,U.S. Pat. No. 6,033,864); secretory vesicle antigens or ANCA-reactivefragments thereof (see, e.g., U.S. patent application Ser. No.08/804,106); and anti-ANCA idiotypic antibodies. One skilled in the artwill appreciate that the use of additional antigens specific for ANCA iswithin the scope of the present invention.

N. Anti-Saccharomyces cerevisiae Antibodies

The determination of ASCA (e.g., ASCA-IgA and/or ASCA-IgG) levels in asample is also useful in the present invention. As used herein, the term“anti-Saccharomyces cerevisiae immunoglobulin A” or “ASCA-IgA” includesantibodies of the immunoglobulin A isotype that react specifically withS. cerevisiae. Similarly, the term “anti-Saccharomyces cerevisiaeimmunoglobulin G” or “ASCA-IgG” includes antibodies of theimmunoglobulin G isotype that react specifically with S. cerevisiae.

The determination of whether a sample is positive for ASCA-IgA orASCA-IgG is made using an antigen specific for ASCA. Such an antigen canbe any antigen or mixture of antigens that is bound specifically byASCA-IgA and/or ASCA-IgG. Although ASCA antibodies were initiallycharacterized by their ability to bind S. cerevisiae, those of skill inthe art will understand that an antigen that is bound specifically byASCA can be obtained from S. cerevisiae or from a variety of othersources so long as the antigen is capable of binding specifically toASCA antibodies. Accordingly, exemplary sources of an antigen specificfor ASCA, which can be used to determine the levels of ASCA-IgA and/orASCA-IgG in a sample, include, without limitation, whole killed yeastcells such as Saccharomyces or Candida cells; yeast cell wall mannansuch as phosphopeptidomannan (PPM); oligosachharides such asoligomannosides; neoglycolipids; anti-ASCA idiotypic antibodies; and thelike. Different species and strains of yeast, such as S. cerevisiaestrain Su1, Su2, CBS 1315, or BM 156, or Candida albicans strain VW32,are suitable for use as an antigen specific for ASCA-IgA and/orASCA-IgG. Purified and synthetic antigens specific for ASCA are alsosuitable for use in determining the levels of ASCA-IgA and/or ASCA-IgGin a sample. Examples of purified antigens include, without limitation,purified oligosaccharide antigens such as oligomannosides. Examples ofsynthetic antigens include, without limitation, syntheticoligomannosides such as those described in U.S. Patent Publication No.20030105060, e.g., D-Man β(1-2) D-Man β(1-2) D-Man β(1-2) D-Man-OR,D-Man α(1-2) D-Man α(1-2) D-Man α(1-2) D-Man-OR, and D-Man α(1-3) D-Manα(1-2) D-Man α(1-2) D-Man-OR, wherein R is a hydrogen atom, a C₁ to C₂₀alkyl, or an optionally labeled connector group.

Preparations of yeast cell wall mannans, e.g., PPM, can be used indetermining the levels of ASCA-IgA and/or ASCA-IgG in a sample. Suchwater-soluble surface antigens can be prepared by any appropriateextraction technique known in the art, including, for example, byautoclaving, or can be obtained commercially (see, e.g., Lindberg etal., Gut, 33:909-913 (1992)). The acid-stable fraction of PPM is alsouseful in the statistical algorithms of the present invention (Sendid etal., Clin. Diag. Lab. Immunol., 3:219-226 (1996)). An exemplary PPM thatis useful in determining ASCA levels in a sample is derived from S.uvarum strain ATCC #38926.

Purified oligosaccharide antigens such as oligomannosides can also beuseful in determining the levels of ASCA-IgA and/or ASCA-IgG in asample. The purified oligomannoside antigens are preferably convertedinto neoglycolipids as described in, for example, Faille et al., Eur. J.Microbiol Infect. Dis., 11:438-446 (1992). One skilled in the artunderstands that the reactivity of such an oligomannoside antigen withASCA can be optimized by varying the mannosyl chain length (Frosh etal., Proc Natl. Acad. Sci. USA, 82:1194-1198 (1985)); the anomericconfiguration (Fukazawa et al., In “Immunology of Fungal Disease,” E.Kurstak (ed.), Marcel Dekker Inc., New York, pp. 37-62 (1989); Nishikawaet al., Microbiol. Immunol., 34:825-840 (1990); Poulain et al., Eur. J.Clin. Microbiol., 23:46-52 (1993); Shibata et al., Arch. Biochem.Biophys., 243:338-348 (1985); Trinel et al., Infect. Immun.,60:3845-3851 (1992)); or the position of the linkage (Kikuchi et al.,Planta, 190:525-535 (1993)).

Suitable oligomannosides for use in the methods of the present inventioninclude, without limitation, an oligomannoside having the mannotetraoseMan(1-3) Man(1-2) Man(1-2) Man. Such an oligomannoside can be purifiedfrom PPM as described in, e.g., Faille et al., supra. An exemplaryneoglycolipid specific for ASCA can be constructed by releasing theoligomannoside from its respective PPM and subsequently coupling thereleased oligomannoside to 4-hexadecylaniline or the like.

O. Anti-Microbial Antibodies

The determination of anti-OmpC antibody levels in a sample is alsouseful in the present invention. As used herein, the term “anti-outermembrane protein C antibody” or “anti-OmpC antibody” includes antibodiesdirected to a bacterial outer membrane porin as described in, e.g., PCTPatent Publication No. WO 01/89361. The term “outer membrane protein C”or “OmpC” refers to a bacterial porin that is immunoreactive with ananti-OmpC antibody.

The level of anti-OmpC antibody present in a sample from an individualcan be determined using an OmpC protein or a fragment thereof such as animmunoreactive fragment thereof. Suitable OmpC antigens useful indetermining anti-OmpC antibody levels in a sample include, withoutlimitation, an OmpC protein, an OmpC polypeptide having substantiallythe same amino acid sequence as the OmpC protein, or a fragment thereofsuch as an immunoreactive fragment thereof. As used herein, an OmpCpolypeptide generally describes polypeptides having an amino acidsequence with greater than about 50% identity, preferably greater thanabout 60% identity, more preferably greater than about 70% identity,still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%,98%, or 99% amino acid sequence identity with an OmpC protein, with theamino acid identity determined using a sequence alignment program suchas CLUSTALW. Such antigens can be prepared, for example, by purificationfrom enteric bacteria such as E. coli, by recombinant expression of anucleic acid such as Genbank Accession No. K00541, by synthetic meanssuch as solution or solid phase peptide synthesis, or by using phagedisplay.

The determination of anti-I2 antibody levels in a sample is also usefulin the present invention. As used herein, the term “anti-I2 antibody”includes antibodies directed to a microbial antigen sharing homology tobacterial transcriptional regulators as described in, e.g., U.S. Pat.No. 6,309,643. The term “I2” refers to a microbial antigen that isimmunoreactive with an anti-I2 antibody. The microbial 12 protein is apolypeptide of 100 amino acids sharing some similarity weak homologywith the predicted protein 4 from C. pasteurianum, Rv3557c fromMycobacterium tuberculosis, and a transcriptional regulator from Aquifexaeolicus. The nucleic acid and protein sequences for the I2 protein aredescribed in, e.g., U.S. Pat. No. 6,309,643.

The level of anti-I2 antibody present in a sample from an individual canbe determined using an I2 protein or a fragment thereof such as animmunoreactive fragment thereof. Suitable I2 antigens useful indetermining anti-I2 antibody levels in a sample include, withoutlimitation, an I2 protein, an I2 polypeptide having substantially thesame amino acid sequence as the I2 protein, or a fragment thereof suchas an immunoreactive fragment thereof. Such I2 polypeptides exhibitgreater sequence similarity to the I2 protein than to the C.pasteurianum protein 4 and include isotype variants and homologsthereof. As used herein, an I2 polypeptide generally describespolypeptides having an amino acid sequence with greater than about 50%identity, preferably greater than about 60% identity, more preferablygreater than about 70% identity, still more preferably greater thanabout 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acid sequenceidentity with a naturally-occurring I2 protein, with the amino acididentity determined using a sequence alignment program such as CLUSTALW.Such I2 antigens can be prepared, for example, by purification frommicrobes, by recombinant expression of a nucleic acid encoding an I2antigen, by synthetic means such as solution or solid phase peptidesynthesis, or by using phage display.

The determination of anti-flagellin antibody levels in a sample is alsouseful in the present invention. As used herein, the term“anti-flagellin antibody” includes antibodies directed to a proteincomponent of bacterial flagella as described in, e.g., PCT PatentPublication No. WO 03/053220 and U.S. Patent Publication No.20040043931. The term “flagellin” refers to a bacterial flagellumprotein that is immunoreactive with an anti-flagellin antibody.Microbial flagellins are proteins found in bacterial flagellum thatarrange themselves in a hollow cylinder to form the filament.

The level of anti-flagellin antibody present in a sample from anindividual can be determined using a flagellin protein or a fragmentthereof such as an immunoreactive fragment thereof. Suitable flagellinantigens useful in determining anti-flagellin antibody levels in asample include, without limitation, a flagellin protein such as Cbir-1flagellin, flagellin X, flagellin A, flagellin B, fragments thereof, andcombinations thereof, a flagellin polypeptide having substantially thesame amino acid sequence as the flagellin protein, or a fragment thereofsuch as an immunoreactive fragment thereof. As used herein, a flagellinpolypeptide generally describes polypeptides having an amino acidsequence with greater than about 50% identity, preferably greater thanabout 60% identity, more preferably greater than about 70% identity,still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%,98%, or 99% amino acid sequence identity with a naturally-occurringflagellin protein, with the amino acid identity determined using asequence alignment program such as CLUSTALW. Such flagellin antigens canbe prepared, e.g., by purification from bacterium such as HelicobacterBilis, Helicobacter mustelae, Helicobacter pylori, Butyrivibriofibrisolvens, and bacterium found in the cecum, by recombinantexpression of a nucleic acid encoding a flagellin antigen, by syntheticmeans such as solution or solid phase peptide synthesis, or by usingphage display.

P. Other Diagnostic Markers

The determination of the presence or level of lactoferrin in a sample isalso useful in the present invention. In certain instances, the presenceor level of lactoferrin is detected at the level of mRNA expression withan assay such as, for example, a hybridization assay or anamplification-based assay. In certain other instances, the presence orlevel of lactoferrin is detected at the level of protein expressionusing, for example, an immunoassay (e.g., ELISA) or animmunohistochemical assay. A lactoferrin ELISA kit available fromCalbiochem (San Diego, Calif.) can be used to detect human lactoferrinin a plasma, urine, bronchoalveolar lavage, or cerebrospinal fluidsample. Similarly, an ELISA kit available from U.S. Biological(Swampscott, Mass.) can be used to determine the level of lactoferrin ina plasma sample. U.S. Patent Publication No. 20040137536 describes anELISA assay for determining the presence of elevated lactoferrin levelsin a stool sample. Likewise, U.S. Patent Publication No. 20040033537describes an ELISA assay for determining the concentration of endogenouslactoferrin in a stool, mucus, or bile sample. In some embodiments, thenpresence or level of anti-lactoferrin antibodies can be detected in asample using, e.g., lactoferrin protein or a fragment thereof.

Immunoassays such as ELISA are also particularly useful for determiningthe presence or level of C-reactive protein (CRP) in a sample. Forexample, a sandwich colorimetric ELISA assay available from AlpcoDiagnostics (Salem, N.H.) can be used to determine the level of CRP in aserum, plasma, urine, or stool sample. Similarly, an ELISA kit availablefrom Biomeda Corporation (Foster City, Calif.) can be used to detect CRPlevels in a sample. Other methods for determining CRP levels in a sampleare described in, e.g., U.S. Pat. Nos. 6,838,250 and 6,406,862; and U.S.Patent Publication Nos. 20060024682 and 20060019410.

In addition, hemoccult, fecal occult blood, is often indicative ofgastrointestinal illness and various kits have been developed to monitorgastrointestinal bleeding. For example, Hemoccult SENSA, a BeckmanCoulter product, is a diagnostic aid for gastrointestinal bleeding, irondeficiency, peptic ulcers, ulcerative colitis, and, in some instances,in screening for colorectal cancer. This particular assay is based onthe oxidation of guaiac by hydrogen peroxide to produce a blue color. Asimilar colorimetric assay is commercially available from HelenaLaboratories (Beaumont, Tex.) for the detection of blood in stoolsamples. Other methods for detecting occult blood in a stool sample bydetermining the presence or level of hemoglobin or heme activity aredescribed in, e.g., U.S. Pat. Nos. 4,277,250, 4,920,045, 5,081,040, and5,310,684.

The determination of the presence or level of fibrinogen or aproteolytic product thereof such as a fibrinopeptide in a sample is alsouseful in the present invention. Fibrinogen is a plasma glycoproteinsynthesized in the liver composed of 3 structurally different subunits:alpha (FGA); beta (FGB); and gamma (FGG). Thrombin causes a limitedproteolysis of the fibrinogen molecule, during which fibrinopeptides Aand B are released from the N-terminal regions of the alpha and betachains, respectively. Fibrinopeptides A and B, which have been sequencedin many species, may have a physiological role as vasoconstrictors andmay aid in local hemostasis during blood clotting. In one embodiment,human fibrinopeptide A comprises the sequence:Ala-Asp-Ser-Gly-Glu-Gly-Asp-Phe-Leu-Ala-Glu-Gly-Gly-Gly-Val-Arg (SEQ IDNO:1). In another embodiment, human fibrinopeptide B comprises thesequence: Glp-Gly-Val-Asn-Asp-Asn-Glu-Glu-Gly-Phe-Phe-Ser-Ala-Arg (SEQID NO:2). An ELISA kit available from American Diagnostica Inc.(Stamford, Conn.) can be used to detect the presence or level of humanfibrinopeptide A in plasma or other biological fluids.

In certain embodiments, the determination of the presence or level ofcalcitonin gene-related peptide (CGRP) in a sample is useful in thepresent invention. Calcitonin is a 32-amino acid peptide honnonesynthesized by the parafollicular cells of the thyroid. It causesreduction in serum calcium, an effect opposite to that of parathyroidhormone. CGRP is derived, with calcitonin, from the CT/CGRP gene locatedon chromosome 11. CGRP is a 37-amino acid peptide and is a potentendogenous vasodilator. CGRP is primarily produced in nervous tissue;however, its receptors are expressed throughout the body. An ELISA kitavailable from Cayman Chemical Co. (Ann Arbor, Mich.) can be used todetect the presence or level of human CGRP in a variety of samplesincluding plasma, serum, nervous tissue, CSF, and culture media.

In other embodiments, the determination of the presence or level of ananti-tissue transglutaminase (tTG) antibody in a sample is useful in thepresent invention. As used herein, the term “anti-tTG antibody” includesany antibody that recognizes tissue transglutaminase (tTG) or a fragmentthereof. Transglutaminases are a diverse family of Ca²⁺-dependentenzymes that are ubiquitous and highly conserved across species. Of allthe transglutaminases, tTG is the most widely distributed. In certaininstances, the anti-tTG antibody is an anti-tTG IgA antibody, anti-tTGIgG antibody, or mixtures thereof. An ELISA kit available from ScheBoBiotech USA Inc. (Marietta, Ga.) can be used to detect the presence orlevel of human anti-tTG IgA antibodies in a blood sample.

The determination of the presence of polymorphisms in the NOD2/CARD15gene in a sample is also useful in the present invention. For example,polymorphisms in the NOD2 gene such as a C2107T nucleotide variant thatresults in a R703W protein variant can be identified in a sample from anindividual (see, e.g., U.S. Patent Publication No. 20030190639). In analternative embodiment, NOD2 mRNA levels can be used as a diagnosticmarker of the present invention to aid in classifying IBS.

The determination of the presence of polymorphisms in the serotoninreuptake transporter (SERT) gene in a sample is also useful in thepresent invention. For example, polymorphisms in the promoter region ofthe SERT gene have effects on transcriptional activity, resulting inaltered 5-HT reuptake efficiency. It has been shown that a stronggenotypic association was observed between the SERT-P deletion/deletiongenotype and the IBS phenotype (see, e.g., Yeo Gut, 53:1396-1399(2004)). In an alternative embodiment, SERT mRNA levels can be used as adiagnostic marker of the present invention to aid in classifying IBS(see, e.g., Gershon, J. Clin. Gastroenterol., 39 (5 Suppl.): S184-193(2005)).

In certain aspects, the level of tryptophan hydroxylase-1 mRNA is adiagnostic marker. For example, tryptophan hydroxylase-1 mRNA has beenshown to be significantly reduced in IBS (see, e.g., Coats,Gastroenterology, 126:1897-1899 (2004)). In certain other aspects, alactulose breath test to measure methane, which is indicative ofbacterial overgrowth, can be used as a diagnostic marker for IBS.

Additional diagnostic markers include, but are not limited to,L-selectin/CD62L, anti-U1-70 kDa autoantibodies, zona occludens 1(ZO-1), vasoactive intestinal peptide (VIP), serum amyloid A, gastrin,NB3 gene polymorphisms, NCI1 gene polymorphisms, fecal leukocytes, α2Aand α2C adrenoreceptor gene polymorphisms, IL-10 gene polymorphisms,TNF-α gene polymorphisms, TGF-β1 gene polymorphisms, a-adrenergicreceptors, G-proteins, 5-HT_(2A) gene polymorphisms, 5-HTT LPR genepolymorphisms, 5-HT₄ receptor gene polymorphisms, zonulin, and the33-mer peptide (Shan et al., Science, 297:2275-2279 (2002); PCT PatentPublication No. WO 03/068170).

VI. Classification Markers

A variety of classification markers are suitable for use in the methods,systems, and code of the present invention for classifying IBS into acategory, form, or clinical subtype such as, for example,IBS-constipation (IBS-C), IBS-diarrhea (IBS-D), IBS-mixed (IBS-M),IBS-alternating (IBS-A), or post-infectious IBS (IBS-PI). Examples ofclassification markers include, without limitation, any of thediagnostic markers described above (e.g., leptin, serotonin reuptaketransporter (SERT), tryptophan hydroxylase-1, 5-hydroxytryptamine(5-HT), and the like), as well as antrum mucosal protein 8, keratin-8,claudin-8, zonulin, corticotropin-releasing hormone receptor-1 (CRHR1),corticotropin-releasing hormone receptor-2 (CRHR2), and the like.

For instance, Example 1 illustrates that measuring leptin levels isparticularly useful for distinguishing IBS-C patient samples from LBS-Aand IBS-D patient samples. In addition, mucosal SERT and tryptophanhydroxylase-1 expression have been shown to be decreased in IBS-C andIBS-D (see, e.g., Gershon, J. Clin. Gastroenterol., 39 (5 Suppl):S184-193 (2005)). Furthermore, IBS-C patients show impaired postprandial5-HT release, whereas IBS-PI patients have higher peak levels of 5-HT(see, e.g., Dunlop, Clin Gastroenterol Hepatol., 3:349-357 (2005)).

VII. Assays

Any of a variety of assays, techniques, and kits known in the art can beused to determine the presence or level of one or more markers in asample to classify whether the sample is associated with IBS.

The present invention relies, in part, on determining the presence orlevel of at least one marker in a sample obtained from an individual. Asused herein, the term “determining the presence of at least one marker”includes determining the presence of each marker of interest by usingany quantitative or qualitative assay known to one of skill in the art.In certain instances, qualitative assays that determine the presence orabsence of a particular trait, variable, or biochemical or serologicalsubstance (e.g., protein or antibody) are suitable for detecting eachmarker of interest. In certain other instances, quantitative assays thatdetermine the presence or absence of RNA, protein, antibody, or activityare suitable for detecting each marker of interest. As used herein, theterm “determining the level of at least one marker” includes determiningthe level of each marker of interest by using any direct or indirectquantitative assay known to one of skill in the art. In certaininstances, quantitative assays that determine, for example, the relativeor absolute amount of RNA, protein, antibody, or activity are suitablefor determining the level of each marker of interest. One skilled in theart will appreciate that any assay useful for determining the level of amarker is also useful for determining the presence or absence of themarker.

As used herein, the term “antibody” includes a population ofimmunoglobulin molecules, which can be polyclonal or monoclonal and ofany isotype, or an immunologically active fragment of an immunoglobulinmolecule. Such an immunologically active fragment contains the heavy andlight chain variable regions, which make up the portion of the antibodymolecule that specifically binds an antigen. For example, animmunologically active fragment of an immunoglobulin molecule known inthe art as Fab, Fab′ or F(ab′)₂ is included within the meaning of theterm antibody.

Flow cytometry can be used to determine the presence or level of one ormore markers in a sample. Such flow cytometric assays, including beadbased immunoassays, can be used to determine, e.g., antibody markerlevels in the same manner as described for detecting serum antibodies toCandida albicans and HIV proteins (see, e.g., Bishop and Davis, J.Immunol. Methods, 210:79-87 (1997); McHugh et al., J. Immunol. Methods,116:213 (1989); Scillian et al., Blood, 73:2041 (1989)).

Phage display technology for expressing a recombinant antigen specificfor a marker can also be used to determine the presence or level of oneor more markers in a sample. Phage particles expressing an antigenspecific for, e.g., an antibody marker can be anchored, if desired, to amulti-well plate using an antibody such as an anti-phage monoclonalantibody (Felici et al., “Phage-Displayed Peptides as Tools forCharacterization of Human Sera” in Abelson (Ed.), Methods in Enzymol.,267, San Diego: Academic Press, Inc. (1996)).

A variety of immunoassay techniques, including competitive andnon-competitive immunoassays, can be used to determine the presence orlevel of one or more markers in a sample (see, e.g., Self and Cook,Curr. Opin. Biotechnol., 7:60-65 (1996)). The term immunoassayencompasses techniques including, without limitation, enzymeimmunoassays (EIA) such as enzyme multiplied immunoassay technique(EMIT), enzyme-linked immunosorbent assay (ELISA), antigen captureELISA, sandwich ELISA, IgM antibody capture ELISA (MAC ELISA), andmicroparticle enzyme immunoassay (MEIA); capillary electrophoresisimmunoassays (CEIA); radioimmunoassays (RIA); immunoradiometric assays(IRMA); fluorescence polarization immunoassays (FPIA); andchemiluminescence assays (CL). If desired, such immunoassays can beautomated. Immunoassays can also be used in conjunction with laserinduced fluorescence (see, e.g., Schmalzing and Nashabeh,Electrophoresis, 18:2184-2193 (1997); Bao, J. Chromatogr. B. Biomed.Sci., 699:463-480 (1997)). Liposome immunoassays, such as flow-injectionliposome immunoassays and liposome immunosensors, are also suitable foruse in the present invention (see, e.g., Rongen et al., J. Immunol.Methods, 204:105-133 (1997)). In addition, nephelometry assays, in whichthe formation of protein/antibody complexes results in increased lightscatter that is converted to a peak rate signal as a function of themarker concentration, are suitable for use in the present invention.Nephelometry assays are commercially available from Beckman Coulter(Brea, CA; Kit #449430) and can be performed using a BehringNephelometer Analyzer (Fink et al., J. Clin. Chem. Clin. Biol. Chem.,27:261-276 (1989)).

Antigen capture ELISA can be useful for determining the presence orlevel of one or more markers in a sample. For example, in an antigencapture ELISA, an antibody directed to a marker of interest is bound toa solid phase and sample is added such that the marker is bound by theantibody. After unbound proteins are removed by washing, the amount ofbound marker can be quantitated using, e.g., a radioimmunoassay (see,e.g., Harlow and Lane, Antibodies: A Laboratory Manual, Cold SpringHarbor Laboratory, New York, 1988)). Sandwich ELISA can also be suitablefor use in the present invention. For example, in a two-antibodysandwich assay, a first antibody is bound to a solid support, and themarker of interest is allowed to bind to the first antibody. The amountof the marker is quantitated by measuring the amount of a secondantibody that binds the marker. The antibodies can be immobilized onto avariety of solid supports, such as magnetic or chromatographic matrixparticles, the surface of an assay plate (e.g., microtiter wells),pieces of a solid substrate material or membrane (e.g., plastic, nylon,paper), and the like. An assay strip can be prepared by coating theantibody or a plurality of antibodies in an array on a solid support.This strip can then be dipped into the test sample and processed quicklythrough washes and detection steps to generate a measurable signal, suchas a colored spot.

A radioimmunoassay using, for example, an iodine-125 (¹²⁵I) labeledsecondary antibody (Harlow and Lane, supra) is also suitable fordetermining the presence or level of one or more markers in a sample. Asecondary antibody labeled with a chemiluminescent marker can also besuitable for use in the present invention. A chemiluminescence assayusing a chemiluminescent secondary antibody is suitable for sensitive,non-radioactive detection of marker levels. Such secondary antibodiescan be obtained commercially from various sources, e.g., AmershamLifesciences, Inc. (Arlington Heights, Ill.).

The immunoassays described above are particularly useful for determiningthe presence or level of one or more markers in a sample. As anon-limiting example, an ELISA using an IL-8-binding molecule such as ananti-IL-8 antibody or an extracellular IL-8-binding protein (e.g., IL-8receptor) is useful for determining whether a sample is positive forIL-8 protein or for determining IL-8 protein levels in a sample. A fixedneutrophil ELISA is useful for determining whether a sample is positivefor ANCA or for determining ANCA levels in a sample. Similarly, an ELISAusing yeast cell wall phosphopeptidomannan is useful for determiningwhether a sample is positive for ASCA-IgA and/or ASCA-IgG, or fordetermining ASCA-IgA and/or ASCA-IgG levels in a sample. An ELISA usingOmpC protein or a fragment thereof is useful for determining whether asample is positive for anti-OmpC antibodies, or for determininganti-OmpC antibody levels in a sample. An ELISA using I2 protein or afragment thereof is useful for determining whether a sample is positivefor anti-I2 antibodies, or for determining anti-I2 antibody levels in asample. An ELISA using flagellin protein (e.g., Cbir-1 flagellin) or afragment thereof is useful for determining whether a sample is positivefor anti-flagellin antibodies, or for determining anti-flagellinantibody levels in a sample. In addition, the immunoassays describedabove are particularly useful for determining the presence or level ofother diagnostic markers in a sample.

Specific immunological binding of the antibody to the marker of interestcan be detected directly or indirectly. Direct labels includefluorescent or luminescent tags, metals, dyes, radionuclides, and thelike, attached to the antibody. An antibody labeled with iodine-125(¹²⁵I) can be used for determining the levels of one or more markers ina sample. A chemiluminescence assay using a chemiluminescent antibodyspecific for the marker is suitable for sensitive, non-radioactivedetection of marker levels. An antibody labeled with fluorochrome isalso suitable for determining the levels of one or more markers in asample. Examples of fluorochromes include, without limitation, DAPI,fluorescein, Hoechst 33258, R-phycocyanin, B-phycoerythrin,R-phycoerythrin, rhodamine, Texas red, and lissamine. Secondaryantibodies linked to fluorochromes can be obtained commercially, e.g.,goat F(ab′)₂ anti-human IgG-FITC is available from Tago Immunologicals(Burlingame, Calif.).

Indirect labels include various enzymes well-known in the art, such ashorseradish peroxidase (HRP), alkaline phosphatase (AP),β-galactosidase, urease, and the like. A horseradish-peroxidasedetection system can be used, for example, with the chromogenicsubstrate tetramethylbenzidine (TMB), which yields a soluble product inthe presence of hydrogen peroxide that is detectable at 450 nm. Analkaline phosphatase detection system can be used with the chromogenicsubstrate p-nitrophenyl phosphate, for example, which yields a solubleproduct readily detectable at 405 nm. Similarly, a β-galactosidasedetection system can be used with the chromogenic substrateo-nitrophenyl-β-D-galactopyranoside (ONPG), which yields a solubleproduct detectable at 410 nm. An urease detection system can be usedwith a substrate such as urea-bromocresol purple (Sigma Immunochemicals;St. Louis, Mo.). A useful secondary antibody linked to an enzyme can beobtained from a number of commercial sources, e.g., goat F(ab′)₂anti-human IgG-alkaline phosphatase can be purchased from JacksonImmunoResearch (West Grove, Pa.).

A signal from the direct or indirect label can be analyzed, for example,using a spectrophotometer to detect color from a chromogenic substrate;a radiation counter to detect radiation such as a gamma counter fordetection of ¹²⁵I; or a fluorometer to detect fluorescence in thepresence of light of a certain wavelength. For detection ofenzyme-linked antibodies, a quantitative analysis of the amount ofmarker levels can be made using a spectrophotometer such as an EMAXMicroplate Reader (Molecular Devices; Menlo Park, Calif.) in accordancewith the manufacturer's instructions. If desired, the assays of thepresent invention can be automated or performed robotically, and thesignal from multiple samples can be detected simultaneously.

Quantitative western blotting can also be used to detect or determinethe presence or level of one or more markers in a sample. Western blotscan be quantitated by well-known methods such as scanning densitometryor phosphorimaging. As a non-limiting example, protein samples areelectrophoresed on 10% SDS-PAGE Laemmli gels. Primary murine monoclonalantibodies are reacted with the blot, and antibody binding can beconfirmed to be linear using a preliminary slot blot experiment. Goatanti-mouse horseradish peroxidase-coupled antibodies (BioRad) are usedas the secondary antibody, and signal detection performed usingchemiluminescence, for example, with the Renaissance chemiluminescencekit (New England Nuclear; Boston, Mass.) according to the manufacturer'sinstructions. Autoradiographs of the blots are analyzed using a scanningdensitometer (Molecular Dynamics; Sunnyvale, Calif.) and normalized to apositive control. Values are reported, for example, as a ratio betweenthe actual value to the positive control (densitometric index). Suchmethods are well known in the art as described, for example, in Parra etal., J. Vasc. Surg., 28:669-675 (1998).

Alternatively, a variety of immunohistochemical assay techniques can beused to determine the presence or level of one or more markers in asample. The term immunohistochemical assay encompasses techniques thatutilize the visual detection of fluorescent dyes or enzymes coupled(i.e., conjugated) to antibodies that react with the marker of interestusing fluorescent microscopy or light microscopy and includes, withoutlimitation, direct fluorescent antibody assay, indirect fluorescentantibody (IFA) assay, anticomplement immunofluorescence, avidin-biotinimmunofluorescence, and immunoperoxidase assays. An IFA assay, forexample, is useful for determining whether a sample is positive forANCA, the level of ANCA in a sample, whether a sample is positive forpANCA, the level of pANCA in a sample, and/or an ANCA staining pattern(e.g., cANCA, pANCA, NSNA, and/or SAPPA staining pattern). Theconcentration of ANCA in a sample can be quantitated, e.g., throughendpoint titration or through measuring the visual intensity offluorescence compared to a known reference standard.

Alternatively, the presence or level of a marker of interest can bedetermined by detecting or quantifying the amount of the purifiedmarker. Purification of the marker can be achieved, for example, by highpressure liquid chromatography (HPLC), alone or in combination with massspectrometry (e.g., MALDI/MS, MALDI-TOF/MS, SELDI-TOF/MS, tandem MS,etc.). Qualitative or quantitative detection of a marker of interest canalso be determined by well-known methods including, without limitation,Bradford assays, Coomassie blue staining, silver staining, assays forradiolabeled protein, and mass spectrometry.

The analysis of a plurality of markers may be carried out separately orsimultaneously with one test sample. For separate or sequential assay ofmarkers, suitable apparatuses include clinical laboratory analyzers suchas the ElecSys (Roche), the AxSym (Abbott), the Access (Beckman), theADVIA®, the CENTAUR® (Bayer), and the NICHOLS ADVANTAGE® (NicholsInstitute) immunoassay systems. Preferred apparatuses or protein chipsperform simultaneous assays of a plurality of markers on a singlesurface. Particularly useful physical formats comprise surfaces having aplurality of discrete, addressable locations for the detection of aplurality of different markers. Such formats include proteinmicroarrays, or “protein chips” (see, e.g., Ng et al., J. Cell Mol.Med., 6:329-340 (2002)) and certain capillary devices (see, e.g., U.S.Pat. No. 6,019,944). In these embodiments, each discrete surfacelocation may comprise antibodies to immobilize one or more markers fordetection at each location. Surfaces may alternatively comprise one ormore discrete particles (e.g., microparticles or nanoparticles)immobilized at discrete locations of a surface, where the microparticlescomprise antibodies to immobilize one or more markers for detection.

In addition to the above-described assays for determining the presenceor level of various markers of interest, analysis of marker mRNA levelsusing routine techniques such as Northern analysis,reverse-transcriptase polymerase chain reaction (RT-PCR), or any othermethods based on hybridization to a nucleic acid sequence that iscomplementary to a portion of the marker coding sequence (e.g., slotblot hybridization) are also within the scope of the present invention.Applicable PCR amplification techniques are described in, e.g., Ausubelet al., Current Protocols in Molecular Biology, John Wiley & Sons, Inc.New York (1999), Chapter 7 and Supplement 47; Theophilus et al., “PCRMutation Detection Protocols,” Humana Press, (2002); and Innis et al.,PCR Protocols, San Diego, Academic Press, Inc. (1990). General nucleicacid hybridization methods are described in Anderson, “Nucleic AcidHybridization,” BIOS Scientific Publishers, 1999. Amplification orhybridization of a plurality of transcribed nucleic acid sequences(e.g., mRNA or cDNA) can also be performed from mRNA or cDNA sequencesarranged in a microarray. Microarray methods are generally described inHardiman, “Microarrays Methods and Applications: Nuts & Bolts,” DNAPress, 2003; and Baldi et al., “DNA Microarrays and Gene Expression:From Experiments to Data Analysis and Modeling,” Cambridge UniversityPress, 2002.

Analysis of the genotype of a marker such as a genetic marker can beperformed using techniques known in the art including, withoutlimitation, polymerase chain reaction (PCR)-based analysis, sequenceanalysis, and electrophoretic analysis. A non-limiting example of aPCR-based analysis includes a Taqman® allelic discrimination assayavailable from Applied Biosystems. Non-limiting examples of sequenceanalysis include Maxam-Gilbert sequencing, Sanger sequencing, capillaryarray DNA sequencing, thermal cycle sequencing (Sears et al.,Biotechniques, 13:626-633 (1992)), solid-phase sequencing (Zimmerman etal., Methods Mol. Cell Biol., 3:39-42 (1992)), sequencing with massspectrometry such as matrix-assisted laser desorption/ionizationtime-of-flight mass spectrometry (MALDI-TOF/MS; Fu et al., NatureBiotech., 16:381-384 (1998)), and sequencing by hybridization (Chee etal., Science, 274:610-614 (1996); Drmanac et al., Science, 260:1649-1652(1993); Drmanac et al., Nature Biotech., 16:54-58 (1998)). Non-limitingexamples of electrophoretic analysis include slab gel electrophoresissuch as agarose or polyacrylamide gel electrophoresis, capillaryelectrophoresis, and denaturing gradient gel electrophoresis. Othermethods for genotyping an individual at a polymorphic site in a markerinclude, e.g., the INVADER® assay from Third Wave Technologies, Inc.,restriction fragment length polymorphism (RFLP) analysis,allele-specific oligonucleotide hybridization, a heteroduplex mobilityassay, and single strand conformational polymorphism (SSCP) analysis.

Several markers of interest may be combined into one test for efficientprocessing of a multiple of samples. In addition, one skilled in the artwould recognize the value of testing multiple samples (e.g., atsuccessive time points, etc.) from the same subject. Such testing ofserial samples can allow the identification of changes in marker levelsover time. Increases or decreases in marker levels, as well as theabsence of change in marker levels, can also provide useful informationto classify IBS or to rule out diseases and disorders associated withIBS-like symptoms.

A panel for measuring one or more of the markers described above may beconstructed to provide relevant infonnation related to the approach ofthe present invention for classifying a sample as being associated withIBS. Such a panel may be constructed to determine the presence or levelof 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 25, 30, 35, 40, or more individual markers. The analysis of a singlemarker or subsets of markers can also be carried out by one skilled inthe art in various clinical settings. These include, but are not limitedto, ambulatory, urgent care, critical care, intensive care, monitoringunit, inpatient, outpatient, physician office, medical clinic, andhealth screening settings.

The analysis of markers could be carried out in a variety of physicalformats as well. For example, the use of microtiter plates or automationcould be used to facilitate the processing of large numbers of testsamples. Alternatively, single sample formats could be developed tofacilitate treatment and diagnosis in a timely fashion.

VIII. Statistical Algorithms

In some aspects, the present invention provides methods, systems, andcode for classifying whether a sample is associated with IBS using astatistical algorithm or process to classify the sample as an IBS sampleor non-IBS sample. In other aspects, the present invention providesmethods, systems, and code for classifying whether a sample isassociated with IBS using a first statistical algorithm or process toclassify the sample as a non-IBD sample or IBD sample (i.e., IBDrule-out step), followed by a second statistical algorithm or process toclassify the non-IBD sample as an IBS sample or non-IBS sample (i.e.,IBS rule-in step). Preferably, the statistical algorithms or processesindependently comprise one or more learning statistical classifiersystems. As described herein, a combination of learning statisticalclassifier systems advantageously provides improved sensitivity,specificity, negative predictive value, positive predictive value,and/or overall accuracy for classifying whether a sample is associatedwith IBS.

The term “statistical algorithm” or “statistical process” includes anyof a variety of statistical analyses used to determine relationshipsbetween variables. In the present invention, the variables are thepresence or level of at least one marker of interest and/or the presenceor severity of at least one IBS-related symptom. Any number of markersand/or symptoms can be analyzed using a statistical algorithm describedherein. For example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, or more biomarkersand/or symptoms can be included in a statistical algorithm. In oneembodiment, logistic regression is used. In another embodiment, linearregression is used. In certain instances, the statistical algorithms ofthe present invention can use a quantile measurement of a particularmarker within a given population as a variable. Quantiles are a set of“cut points” that divide a sample of data into groups containing (as faras possible) equal numbers of observations. For example, quartiles arevalues that divide a sample of data into four groups containing (as faras possible) equal numbers of observations. The lower quartile is thedata value a quarter way up through the ordered data set; the upperquartile is the data value a quarter way down through the ordered dataset. Quintiles are values that divide a sample of data into five groupscontaining (as far as possible) equal numbers of observations. Thepresent invention can also include the use of percentile ranges ofmarker levels (e.g., tertiles, quartile, quintiles, etc.), or theircumulative indices (e.g., quartile sums of marker levels, etc.) asvariables in the algorithms (just as with continuous variables).

Preferably, the statistical algorithms of the present invention compriseone or more learning statistical classifier systems. As used herein, theterm “learning statistical classifier system” includes a machinelearning algorithmic technique capable of adapting to complex data sets(e.g., panel of markers of interest and/or list of IBS-related symptoms)and making decisions based upon such data sets. In some embodiments, asingle learning statistical classifier system such as a classificationtree (e.g., random forest) is used. In other embodiments, a combinationof 2, 3, 4, 5, 6, 7, 8, 9, 10, or more learning statistical classifiersystems are used, preferably in tandem. Examples of learning statisticalclassifier systems include, but are not limited to, those usinginductive learning (e.g., decision/classification trees such as randomforests, classification and regression trees (C&RT), boosted trees,etc.), Probably Approximately Correct (PAC) learning, connectionistlearning (e.g., neural networks (NN), artificial neural networks (ANN),neuro fuzzy networks (NFN), network structures, perceptrons such asmulti-layer perceptrons, multi-layer feed-forward networks, applicationsof neural networks, Bayesian learning in belief networks, etc.),reinforcement learning (e.g., passive learning in a known environmentsuch as naive learning, adaptive dynamic learning, and temporaldifference learning, passive learning in an unknown environment, activelearning in an unknown environment, learning action-value functions,applications of reinforcement learning, etc.), and genetic algorithmsand evolutionary programming. Other learning statistical classifiersystems include support vector machines (e.g., Kernel methods),multivariate adaptive regression splines (MARS), Levenberg-Marquardtalgorithms, Gauss-Newton algorithms, mixtures of Gaussians, gradientdescent algorithms, and learning vector quantization (LVQ).

Random forests are learning statistical classifier systems that areconstructed using an algorithm developed by Leo Breiman and AdeleCutler. Random forests use a large number of individual decision treesand decide the class by choosing the mode (i.e., most frequentlyoccurring) of the classes as determined by the individual trees. Randomforest analysis can be performed, e.g., using the RandomForests softwareavailable from Salford Systems (San Diego, Calif.). See, e.g., Breiman,Machine Learning, 45:5-32 (2001); andhttp://stat-www.berkeley.edu/users/breiman/RandomForests/cc_home.htm,for a description of random forests.

Classification and regression trees represent a computer intensivealternative to fitting classical regression models and are typicallyused to determine the best possible model for a categorical orcontinuous response of interest based upon one or more predictors.Classification and regression tree analysis can be performed, e.g.,using the CART software available from Salford Systems or the Statisticadata analysis software available from StatSoft, Inc. (Tulsa, Okla.). Adescription of classification and regression trees is found, e.g., inBreiman et al. “Classification and Regression Trees,” Chapman and Hall,New York (1984); and Steinberg et al., “CART: Tree-StructuredNon-Parametric Data Analysis,” Salford Systems, San Diego, (1995).

Neural networks are interconnected groups of artificial neurons that usea mathematical or computational model for information processing basedon a connectionist approach to computation. Typically, neural networksare adaptive systems that change their structure based on external orinternal information that flows through the network. Specific examplesof neural networks include feed-forward neural networks such asperceptrons, single-layer perceptrons, multi-layer perceptrons,backpropagation networks, ADALINE networks, MADALINE networks,Learnmatrix networks, radial basis function (RBF) networks, andself-organizing maps or Kohonen self-organizing networks; recurrentneural networks such as simple recurrent networks and Hopfield networks;stochastic neural networks such as Boltzmann machines; modular neuralnetworks such as committee of machines and associative neural networks;and other types of networks such as instantaneously trained neuralnetworks, spiking neural networks, dynamic neural networks, andcascading neural networks. Neural network analysis can be performed,e.g., using the Statistica data analysis software available fromStatSoft, Inc. See, e.g., Freeman et al., In “Neural Networks:Algorithms, Applications and Programming Techniques,” Addison-WesleyPublishing Company (1991); Zadeh, Information and Control, 8:338-353(1965); Zadeh, “IEEE Trans. on Systems, Man and Cybernetics,” 3:28-44(1973); Gersho et al., In “Vector Quantization and Signal Compression,”Kluywer Academic Publishers, Boston, Dordrecht, London (1992); andHassoun, “Fundamentals of Artificial Neural Networks,” MIT Press,Cambridge, Mass., London (1995), for a description of neural networks.

Support vector machines are a set of related supervised learningtechniques used for classification and regression and are described,e.g., in Cristianini et al., “An Introduction to Support Vector Machinesand Other Kernel-Based Learning Methods,” Cambridge University Press(2000). Support vector machine analysis can be performed, e.g., usingthe SVM^(light) software developed by Thorsten Joachims (CornellUniversity) or using the LIBSVM software developed by Chih-Chung Changand Chih-Jen Lin (National Taiwan University).

The learning statistical classifier systems described herein can betrained and tested using a cohort of samples (e.g., serological samples)from healthy individuals, IBS patients, IBD patients, and/or Celiacdisease patients. For example, samples from patients diagnosed by aphysician, and preferably by a gastroenterologist as having IBD using abiopsy, colonoscopy, or an immunoassay as described in, e.g., U.S. Pat.No. 6,218,129, are suitable for use in training and testing the learningstatistical classifier systems of the present invention. Samples frompatients diagnosed with IBD can also be stratified into Crohn's diseaseor ulcerative colitis using an immunoassay as described in, e.g., U.S.Pat. Nos. 5,750,355 and 5,830,675. Samples from patients diagnosed withIBS using a published criteria such as the Manning, Rome I, Rome II, orRome III diagnostic criteria are suitable for use in training andtesting the learning statistical classifier systems of the presentinvention. Samples from healthy individuals can include those that werenot identified as IBD and/or IBS samples. One skilled in the art willknow of additional techniques and diagnostic criteria for obtaining acohort of patient samples that can be used in training and testing thelearning statistical classifier systems of the present invention.

As used herein, the term “sensitivity” refers to the probability that adiagnostic method, system, or code of the present invention gives apositive result when the sample is positive, e.g., having IBS.Sensitivity is calculated as the number of true positive results dividedby the sum of the true positives and false negatives. Sensitivityessentially is a measure of how well a method, system, or code of thepresent invention correctly identifies those with IBS from those withoutthe disease. The statistical algorithms can be selected such that thesensitivity of classifying IBS is at least about 60%, and can be, forexample, at least about 65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%,82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%,96%, 97%, 98%, or 99%. In preferred embodiments, the sensitivity ofclassifying IBS is at least about 90% when a combination of learningstatistical classifier systems is used (see, Example 10) or at leastabout 85% when a single learning statistical classifier system is used(see, Example 11).

The term “specificity” refers to the probability that a diagnosticmethod, system, or code of the present invention gives a negative resultwhen the sample is not positive, e.g., not having IBS. Specificity iscalculated as the number of true negative results divided by the sum ofthe true negatives and false positives. Specificity essentially is ameasure of how well a method, system, or code of the present inventionexcludes those who do not have IBS from those who have the disease. Thestatistical algorithms can be selected such that the specificity ofclassifying IBS is at least about 70%, for example, at least about 75%,80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,98%, or 99%. In preferred embodiments, the specificity of classifyingIBS is at least about 86% when a combination of learning statisticalclassifier systems is used (see, Example 10) or at least about 84% whena single learning statistical classifier system is used (see, Example11).

As used herein, the term “negative predictive value” or “NPV” refers tothe probability that an individual identified as not having IBS actuallydoes not have the disease. Negative predictive value can be calculatedas the number of true negatives divided by the sum of the true negativesand false negatives. Negative predictive value is determined by thecharacteristics of the diagnostic method, system, or code as well as theprevalence of the disease in the population analyzed. The statisticalalgorithms can be selected such that the negative predictive value in apopulation having a disease prevalence is in the range of about 70% toabout 99% and can be, for example, at least about 70%, 75%, 76%, 77%,78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%,92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. In preferred embodiments, thenegative predictive value of classifying IBS is at least about 87% whena combination of learning statistical classifier systems is used (see,Example 10).

The term “positive predictive value” or “PPV” refers to the probabilitythat an individual identified as having IBS actually has the disease.Positive predictive value can be calculated as the number of truepositives divided by the sum of the true positives and false positives.Positive predictive value is determined by the characteristics of thediagnostic method, system, or code as well as the prevalence of thedisease in the population analyzed. The statistical algorithms can beselected such that the positive predictive value in a population havinga disease prevalence is in the range of about 80% to about 99% and canbe, for example, at least about 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%,92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. In preferred embodiments, thepositive predictive value of classifying IBS is at least about 90% whena combination of learning statistical classifier systems is used (see,Example 10).

Predictive values, including negative and positive predictive values,are influenced by the prevalence of the disease in the populationanalyzed. In the methods, systems, and code of the present invention,the statistical algorithms can be selected to produce a desired clinicalparameter for a clinical population with a particular IBS prevalence.For example, learning statistical classifier systems can be selected foran IBS prevalence of up to about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%,10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, or 70%,which can be seen, e.g., in a clinician's office such as agastroenterologist's office or a general practitioner's office.

As used herein, the term “overall agreement” or “overall accuracy”refers to the accuracy with which a method, system, or code of thepresent invention classifies a disease state. Overall accuracy iscalculated as the sum of the true positives and true negatives dividedby the total number of sample results and is affected by the prevalenceof the disease in the population analyzed. For example, the statisticalalgorithms can be selected such that the overall accuracy in a patientpopulation having a disease prevalence is at least about 60%, and canbe, for example, at least about 65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%,81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%,95%, 96%, 97%, 98%, or 99%. In preferred embodiments, the overallaccuracy of classifying IBS is at least about 80% when a combination oflearning statistical classifier systems is used (see, Example 10).

IX. Disease Classification System

FIG. 2 illustrates a disease classification system (DCS) (200) accordingto one embodiment of the present invention. As shown therein, a DCSincludes a DCS intelligence module (205), such as a computer, having aprocessor (215) and memory module (210). The intelligence module alsoincludes communication modules (not shown) for transmitting andreceiving information over one or more direct connections (e.g., USB,Firewire, or other interface) and one or more network connections (e.g.,including a modem or other network interface device). The memory modulemay include internal memory devices and one or more external memorydevices. The intelligence module also includes a display module (225),such as a monitor or printer. In one aspect, the intelligence modulereceives data such as patient test results from a data acquisitionmodule such as a test system (250), either through a direct connectionor over a network (240). For example, the test system may be configuredto run multianalyte tests on one or more patient samples (255) andautomatically provide the test results to the intelligence module. Thedata may also be provided to the intelligence module via direct input bya user or it may be downloaded from a portable medium such as a compactdisk (CD) or a digital versatile disk (DVD). The test system may beintegrated with the intelligence module, directly coupled to theintelligence module, or it may be remotely coupled with the intelligencemodule over the network. The intelligence module may also communicatedata to and from one or more client systems (230) over the network as iswell known. For example, a requesting physician or healthcare providermay obtain and view a report from the intelligence module, which may beresident in a laboratory or hospital, using a client system (230).

The network can be a LAN (local area network), WAN (wide area network),wireless network, point-to-point network, star network, token ringnetwork, hub network, or other configuration. As the most common type ofnetwork in current use is a TCP/IP (Transfer Control Protocol andInternet Protocol) network such as the global internetwork of networksoften referred to as the “Internet” with a capital “I,” that will beused in many of the examples herein, but it should be understood thatthe networks that the present invention might use are not so limited,although TCP/IP is the currently preferred protocol.

Several elements in the system shown in FIG. 2 may include conventional,well-known elements that need not be explained in detail here. Forexample, the intelligence module could be implemented as a desktoppersonal computer, workstation, mainframe, laptop, etc. Each clientsystem could include a desktop personal computer, workstation, laptop,PDA, cell phone, or any WAP-enabled device or any other computing devicecapable of interfacing directly or indirectly to the Internet or othernetwork connection. A client system typically runs an HTTP client, e.g.,a browsing program, such as Microsoft's Internet Explorer™ browser,Netscape's Navigator™ browser, Opera's browser, or a WAP-enabled browserin the case of a cell phone, PDA or other wireless device, or the like,allowing a user of the client system to access, process, and viewinformation and pages available to it from the intelligence module overthe network. Each client system also typically includes one or more userinterface devices, such as a keyboard, a mouse, touch screen, pen or thelike, for interacting with a graphical user interface (GUI) provided bythe browser on a display (e.g., monitor screen, LCD display, etc.) (235)in conjunction with pages, forms, and other information provided by theintelligence module. As discussed above, the present invention issuitable for use with the Internet, which refers to a specific globalinternetwork of networks. However, it should be understood that othernetworks can be used instead of the Internet, such as an intranet, anextranet, a virtual private network (VPN), a non-TCP/IP based network,any LAN or WAN, or the like.

According to one embodiment, each client system and all of itscomponents are operator configurable using applications, such as abrowser, including computer code run using a central processing unitsuch as an Intel® Pentium® processor or the like. Similarly, theintelligence module and all of its components might be operatorconfigurable using application(s) including computer code run using acentral processing unit (215) such as an Intel Pentium processor or thelike, or multiple processor units. Computer code for operating andconfiguring the intelligence module to process data and test results asdescribed herein is preferably downloaded and stored on a hard disk, butthe entire program code, or portions thereof, may also be stored in anyother volatile or non-volatile memory medium or device as is well known,such as a ROM or RAM, or provided on any other computer readable medium(260) capable of storing program code, such as a compact disk (CD)medium, digital versatile disk (DVD) medium, a floppy disk, ROM, RAM,and the like.

The computer code for implementing various aspects and embodiments ofthe present invention can be implemented in any programming languagethat can be executed on a computer system such as, for example, in C,C++, C#, HTML, Java, JavaScript, or any other scripting language, suchas VBScript. Additionally, the entire program code, or portions thereof,may be embodied as a carrier signal, which may be transmitted anddownloaded from a software source (e.g., server) over the Internet, orover any other 25 conventional network connection as is well known(e.g., extranet, VPN, LAN, etc.) using any communication medium andprotocols (e.g., TCP/I P, HTTP, HTTPS, Ethernet, etc.) as are wellknown.

According to one embodiment, the intelligence module implements adisease classification process for analyzing patient test results and/orquestionnaire responses to determine whether a patient sample isassociated with IBS. The data may be stored in one or more data tablesor other logical data structures in memory (210) or in a separatestorage or database system coupled with the intelligence module. One ormore statistical processes are typically applied to a data set includingtest data for a particular patient. For example, the test data mightinclude a diagnostic marker profile, which comprises data indicating thepresence or level of at least one marker in a sample from the patient.The test data might also include a symptom profile, which comprises dataindicating the presence or severity of at least one symptom associatedwith IBS that the patient is experiencing or has recently experienced.In one aspect, a statistical process produces a statistically deriveddecision classifying the patient sample as an IBS sample or non-IBSsample based upon the diagnostic marker profile and/or symptom profile.In another aspect, a first statistical process produces a firststatistically derived decision classifying the patient sample as an IBDsample or non-IBD sample based upon the diagnostic marker profile and/orsymptom profile. If the patient sample is classified as a non-IBDsample, a second statistical process is applied to the same or adifferent data set to produce a second statistically derived decisionclassifying the non-IBD sample as an IBS sample or non-IBS sample. Thefirst and/or the second statistically derived decision may be displayedon a display device associated with or coupled to the intelligencemodule, or the decision(s) may be provided to and displayed at aseparate system, e.g., a client system (230). The displayed resultsallow a physician to make a reasoned diagnosis or prognosis.

X. Therapy and Therapeutic Monitoring

Once a sample from an individual has been classified as an IBS sample,the methods, systems, and code of the present invention can furthercomprise administering to the individual a therapeutically effectiveamount of a drug useful for treating one or more symptoms associatedwith IBS (i.e., an IBS drug). For therapeutic applications, the IBS drugcan be administered alone or co-administered in combination with one ormore additional IBS drugs and/or one or more drugs that reduce theside-effects associated with the IBS drug.

IBS drugs can be administered with a suitable pharmaceutical excipientas necessary and can be carried out via any of the accepted modes ofadministration. Thus, administration can be, for example, intravenous,topical, subcutaneous, transcutaneous, transdermal, intramuscular, oral,buccal, sublingual, gingival, palatal, intra-joint, parenteral,intra-arteriole, intradermal, intraventricular, intracranial,intraperitoneal, intralesional, intranasal, rectal, vaginal, or byinhalation. By “co-administer” it is meant that an IBS drug isadministered at the same time, just prior to, or just after theadministration of a second drug (e.g., another IBS drug, a drug usefulfor reducing the side-effects of the IBS drug, etc.).

A therapeutically effective amount of an IBS drug may be administeredrepeatedly, e.g., at least 2, 3, 4, 5, 6, 7, 8, or more times, or thedose may be administered by continuous infusion. The dose may take theform of solid, semi-solid, lyophilized powder, or liquid dosage forms,such as, for example, tablets, pills, pellets, capsules, powders,solutions, suspensions, emulsions, suppositories, retention enemas,creams, ointments, lotions, gels, aerosols, foams, or the like,preferably in unit dosage forms suitable for simple administration ofprecise dosages.

As used herein, the term “unit dosage form” refers to physicallydiscrete units suitable as unitary dosages for human subjects and othermammals, each unit containing a predetermined quantity of an IBS drugcalculated to produce the desired onset, tolerability, and/ortherapeutic effects, in association with a suitable pharmaceuticalexcipient (e.g., an ampoule). In addition, more concentrated dosageforms may be prepared, from which the more dilute unit dosage forms maythen be produced. The more concentrated dosage forms thus will containsubstantially more than, e.g., at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,or more times the amount of the IBS drug.

Methods for preparing such dosage forms are known to those skilled inthe art (see, e.g., R EMINGTON'S PHARMACEUTICAL SCIENCES, 18TH ED., MackPublishing Co., Easton, Pa. (1990)). The dosage forms typically includea conventional pharmaceutical carrier or excipient and may additionallyinclude other medicinal agents, carriers, adjuvants, diluents, tissuepenneation enhancers, solubilizers, and the like. Appropriate excipientscan be tailored to the particular dosage fonn and route ofadministration by methods well known in the art (see, e.g., R EMINGTON'SPHARMACEUTICAL SCIENCES, supra).

Examples of suitable excipients include, but are not limited to,lactose, dextrose, sucrose, sorbitol, mannitol, starches, gum acacia,calcium phosphate, alginates, tragacanth, gelatin, calcium silicate,microcrystalline cellulose, polyvinylpyrrolidone, cellulose, water,saline, syrup, methylcellulose, ethylcellulose,hydroxypropylmethylcellulose, and polyacrylic acids such as Carbopols,e.g., Carbopol 941, Carbopol 980, Carbopol 981, etc. The dosage formscan additionally include lubricating agents such as talc, magnesiumstearate, and mineral oil; wetting agents; emulsifying agents;suspending agents; preserving agents such as methyl-, ethyl-, andpropyl-hydroxy-benzoates (i.e., the parabens); pH adjusting agents suchas inorganic and organic acids and bases; sweetening agents; andflavoring agents. The dosage forms may also comprise biodegradablepolymer beads, dextran, and cyclodextrin inclusion complexes.

For oral administration, the therapeutically effective dose can be inthe form of tablets, capsules, emulsions, suspensions, solutions,syrups, sprays, lozenges, powders, and sustained-release formulations.Suitable excipients for oral administration include pharmaceuticalgrades of mannitol, lactose, starch, magnesium stearate, sodiumsaccharine, talcum, cellulose, glucose, gelatin, sucrose, magnesiumcarbonate, and the like.

In some embodiments, the therapeutically effective dose takes the formof a pill, tablet, or capsule, and thus, the dosage form can contain,along with an IBS drug, any of the following: a diluent such as lactose,sucrose, dicalcium phosphate, and the like; a disintegrant such asstarch or derivatives thereof; a lubricant such as magnesium stearateand the like; and a binder such a starch, gum acacia,polyvinylpyrrolidone, gelatin, cellulose and derivatives thereof. An IBSdrug can also be formulated into a suppository disposed, for example, ina polyethylene glycol (PEG) carrier.

Liquid dosage forms can be prepared by dissolving or dispersing an IBSdrug and optionally one or more pharmaceutically acceptable adjuvants ina carrier such as, for example, aqueous saline (e.g., 0.9% w/v sodiumchloride), aqueous dextrose, glycerol, ethanol, and the like, to form asolution or suspension, e.g., for oral, topical, or intravenousadministration. An IBS drug can also be formulated into a retentionenema.

For topical administration, the therapeutically effective dose can be inthe fonn of emulsions, lotions, gels, foams, creams, jellies, solutions,suspensions, ointments, and transdermal patches. For administration byinhalation, an IBS drug can be delivered as a dry powder or in liquidform via a nebulizer. For parenteral administration, the therapeuticallyeffective dose can be in the form of sterile injectable solutions andsterile packaged powders. Preferably, injectable solutions areformulated at a pH of from about 4.5 to about 7.5.

The therapeutically effective dose can also be provided in a lyophilizedform. Such dosage forms may include a buffer, e.g., bicarbonate, forreconstitution prior to administration, or the buffer may be included inthe lyophilized dosage form for reconstitution with, e.g., water. Thelyophilized dosage form may further comprise a suitable vasoconstrictor,e.g., epinephrine. The lyophilized dosage fonn can be provided in asyringe, optionally packaged in combination with the buffer forreconstitution, such that the reconstituted dosage form can beimmediately administered to an individual.

In therapeutic use for the treatment of IBS, an IBS drug can beadministered at the initial dosage of from about 0.001 mg/kg to about1000 mg/kg daily. A daily dose range of from about 0.01 mg/kg to about500 mg/kg, from about 0.1 mg/kg to about 200 mg/kg, from about 1 mg/kgto about 100 mg/kg, or from about 10 mg/kg to about 50 mg/kg, can beused. The dosages, however, may be varied depending upon therequirements of the individual, the severity of IBS symptoms, and theIBS drug being employed. For example, dosages can be empiricallydetermined considering the severity of IBS symptoms in an individualclassified as having IBS according to the methods described herein. Thedose administered to an individual, in the context of the presentinvention, should be sufficient to affect a beneficial therapeuticresponse in the individual over time. The size of the dose can also bedetermined by the existence, nature, and extent of any adverseside-effects that accompany the administration of a particular IBS drugin an individual. Determination of the proper dosage for a particularsituation is within the skill of the practitioner. Generally, treatmentis initiated with smaller dosages which are less than the optimum doseof the IBS drug. Thereafter, the dosage is increased by small incrementsuntil the optimum effect under circumstances is reached. Forconvenience, the total daily dosage may be divided and administered inportions during the day, if desired.

As used herein, the tenn “IBS drug” includes all pharmaceuticallyacceptable forms of a drug that is useful for treating one or moresymptoms associated with IBS. For example, the IBS drug can be in aracemic or isomeric mixture, a solid complex bound to an ion exchangeresin, or the like. In addition, the IBS drug can be in a solvated form.The term “IBS drug” is also intended to include all pharmaceuticallyacceptable salts, derivatives, and analogs of the IBS drug beingdescribed, as well as combinations thereof. For example, thepharmaceutically acceptable salts of an IBS drug include, withoutlimitation, the tartrate, succinate, tartarate, bitartarate,dihydrochloride, salicylate, hemisuccinate, citrate, maleate,hydrochloride, carbamate, sulfate, nitrate, and benzoate salt formsthereof, as well as combinations thereof and the like. Any form of anIBS drug is suitable for use in the methods of the present invention,e.g., a pharmaceutically acceptable salt of an IBS drug, a free base ofan IBS drug, or a mixture thereof.

Suitable drugs that are useful for treating one or more symptomsassociated with IBS include, but are not limited to, serotonergicagents, antidepressants, chloride channel activators, chloride channelblockers, guanylate cyclase agonists, antibiotics, opioids, neurokininantagonists, antispasmodic or anticholinergic agents, belladonnaalkaloids, barbiturates, glucagon-like peptide-1 (GLP-1) analogs,corticotropin releasing factor (CRF) antagonists, probiotics, free basesthereof, pharmaceutically acceptable salts thereof, derivatives thereof,analogs thereof, and combinations thereof. Other IBS drugs includebulking agents, dopamine antagonists, carminatives, tranquilizers,dextofisopam, phenytoin, timolol, and diltiazem.

Serotonergic agents are useful for the treatment of IBS symptoms such asconstipation, diarrhea, and/or alternating constipation and diarrhea.Non-limiting examples of serotonergic agents are described in Cash etal., Aliment. Pharmacol. Ther., 22:1047-1060 (2005), and include 5-HT₃receptor agonists (e.g., MKC-733, etc.), 5-HT₄ receptor agonists (e.g.,tegaserod (Zelnorm™), prucalopride, AG1-001, etc.), 5-HT₃ receptorantagonists (e.g., alosetron (Lotronex®), cilansetron, ondansetron,granisetron, dolasetron, ramosetron, palonosetron, E-3620, DDP-225,DDP-733, etc.), mixed 5-HT₃ receptor antagonists/5-HT₄ receptor agonists(e.g., cisapride, mosapride, renzapride, etc.), free bases thereof,pharmaceutically acceptable salts thereof, derivatives thereof, analogsthereof, and combinations thereof. Additionally, amino acids likeglutamine and glutamic acid which regulate intestinal permeability byaffecting neuronal or glial cell signaling can be administered to treatpatients with IBS.

Antidepressants such as selective serotonin reuptake inhibitor (SSRI) ortricyclic antidepressants are particularly useful for the treatment ofIBS symptoms such as abdominal pain, constipation, and/or diarrhea.Non-limiting examples of SSRI antidepressants include citalopram,fluvoxamine, paroxetine, fluoxetine, sertraline, free bases thereof,pharmaceutically acceptable salts thereof, derivatives thereof, analogsthereof, and combinations thereof. Examples of tricyclic antidepressantsinclude, but are not limited to, desipramine, nortriptyline,protriptyline, amitriptyline, clomipramine, doxepin, imipramine,trimipramine, maprotiline, amoxapine, clomipramine, free bases thereof,pharmaceutically acceptable salts thereof, derivatives thereof, analogsthereof, and combinations thereof.

Chloride channel activators are useful for the treatment of IBS symptomssuch as constipation. A non-limiting example of a chloride channelactivator is lubiprostone (Amitiza™), a free base thereof, apharmaceutically acceptable salt thereof, a derivative thereof, or ananalog thereof. In addition, chloride channel blockers such ascrofelemer are useful for the treatment of IBS symptoms such asdiarrhea. Guanylate cyclase agonists such as MD-1100 are useful for thetreatment of constipation associated with IBS (see, e.g., Bryant et al.,Gastroenterol., 128:A-257 (2005)). Antibiotics such as neomycin can alsobe suitable for use in treating constipation associated with IBS (see,e.g., Park et al., Gastroenterol., 128:A-258 (2005)). Non-absorbableantibiotics like rifaximin (Xifaxan™) are suitable to treat small bowelbacterial overgrowth and/or constipation associated with IBS (see, e.g.,Sharara et al., Am. J. Gastroenterol., 101:326-333 (2006)).

Opioids such as kappa opiods (e.g., asimadoline) may be useful fortreating pain and/or constipation associated with IBS. Neurokinin (NK)antagonists such as talnetant, saredutant, and other NK2 and/or NK3antagonists may be useful for treating IBS symptoms such asoversensitivity of the muscles in the colon, constipation, and/ordiarrhea. Antispasmodic or anticholinergic agents such as dicyclominemay be useful for treating IBS symptoms such as spasms in the muscles ofthe gut and bladder. Other antispasmodic or anticholinergic agents suchas belladonna alkaloids (e.g., atropine, scopolamine, hyoscyamine, etc.)can be used in combination with barbiturates such as phenobarbital toreduce bowel spasms associated with IBS. GLP-1 analogs such as GTP-010may be useful for treating IBS symptoms such as constipation. CRFantagonists such as astressin and probiotics such as VSL#3® may beuseful for treating one or more IBS symptoms. One skilled in the artwill know of additional IBS drugs currently in use or in developmentthat are suitable for treating one or more symptoms associated with IBS.

An individual can also be monitored at periodic time intervals to assessthe efficacy of a certain therapeutic regimen once a sample from theindividual has been classified as an IBS sample. For example, the levelsof certain markers change based on the therapeutic effect of a treatmentsuch as a drug. The patient is monitored to assess response andunderstand the effects of certain drugs or treatments in anindividualized approach. Additionally, patients may not respond to adrug, but the markers may change, suggesting that these patients belongto a special population (not responsive) that can be identified by theirmarker levels. These patients can be discontinued on their currenttherapy and alternative treatments prescribed.

XI. EXAMPLES

The following examples are offered to illustrate, but not to limit, theclaimed invention.

Example 1 Leptin Discriminates Between IBS and Non-IBS Patient Samples

This example illustrates that determining the presence or level ofleptin is useful for classifying a patient sample as an IBS sample,e.g., by ruling in IBS. The concentration of leptin was measured inserum samples from normal, IBS, IBD (i.e., CD, UC), and Celiac diseasepatients using an immunoassay (i.e., ELISA). As shown in FIG. 3,quartile analysis revealed that leptin levels were elevated in IBSsamples relative to non-IBS (i.e., CD, UC, Celiac disease, normal)samples. Thus, leptin can advantageously discriminate between IBS andnon-IBS samples.

Leptin is also useful for distinguishing between various forms of IBS.FIG. 4A shows the results of an ELISA where leptin levels were measuredin normal, IBD (i.e., CD, UC), and Celiac disease patient samples andsamples from patients having IBS-A, IBS-C, or IBS-D. Leptin levels wereelevated in IBS-A and IBS-D patient samples relative to IBS-C samples.FIG. 4B shows the differences of leptin levels between samples fromfemale IBS patients compared to and male IBS patients.

Example 2 TWEAK Discriminates Between IBS and Non-IBS Patient Samples

This example illustrates that determining the presence or level of TWEAKis useful for classifying a patient sample as an IBS sample, e.g., byruling in IBS. The concentration of TWEAK was measured in samples fromnormal, GI control, IBS, and IBD (i.e., CD, UC) patients using animmunoassay (i.e., ELISA). As shown in FIG. 5, quartile analysisrevealed that TWEAK levels were elevated in IBS samples relative tonon-IBS (i.e., CD, UC, GI control, normal) samples. Thus, TWEAK canadvantageously discriminate between IBS and non-IBS samples.

Example 3 IL-8 Discriminates Between IBS and Normal Patient Samples

This example illustrates that determining the presence or level of IL-8is useful for classifying a patient sample as an IBS sample, e.g., byruling in IBS. The concentration of IL-8 was measured in samples fromnormal, GI control, IBS, IBD (i.e., CD, UC), and Celiac disease patientsusing an immunoassay (i.e., ELISA). As shown in FIG. 6A, quartileanalysis revealed that IL-8 levels were elevated in IBS samples relativeto normal samples. Thus, IL-8 can advantageously discriminate betweenIBS and normal patient samples.

FIG. 6B shows a cumulative percent histogram analysis demonstrating thatIL-8 discriminates about 45% of IBS patient samples from normal patientsamples at a cutoff level of 40 pg/ml. IL-8 can also discriminate about55% of Celiac disease patient samples from normal patient samples at thesame cutoff level. FIG. 7 shows a cumulative percent histogram analysisdemonstrating that IL-8 discriminates about 80% of IBS patient samplesfrom normal patient samples at a cutoff level of 30 pg/ml. An exemplarymethod for performing the cumulative percent histogram analysis isprovided below.

FIG. 8 shows the results of an ELISA where IL-8 levels were measured inhealthy control patient samples and samples from patients having IBS-D,IBS-C, or IBS-A. IL-8 levels were elevated in IBS-D, IBS-C, and IBS-Apatient samples relative to control samples.

Example 4 EGF Discriminates Between IBS and IBD Patient Samples

This example illustrates that determining the presence or level of EGFis useful for classifying a patient sample as an IBS sample, e.g., byruling in IBS or ruling out IBD. The concentration of EGF was measuredin samples from normal, GI control, IBS, IBD (i.e., CD, UC), and Celiacdisease patients using an immunoassay (i.e., ELISA). As shown in FIG.9A, quartile analysis revealed that EGF levels were lower in IBS samplesrelative to IBD samples. Thus, EGF can advantageously discriminatebetween IBS and IBD patient samples.

FIG. 9B shows a cumulative percent histogram analysis demonstrating thatEGF discriminates about 60% of lBS patient samples from IBD patientsamples at a cutoff level of 300 pg/ml. EGF can also discriminate about45% of Celiac disease patient samples from normal patient samples at thesame cutoff level. An exemplary method for performing the cumulativepercent histogram analysis is provided below.

Example 5 NGAL Discriminates Between IBS and Normal Patient Samples

This example illustrates that determining the presence or level of NGALis useful for classifying a patient sample as an IBS sample, e.g., byruling in IBS. The concentration of NGAL was measured in samples fromnonnal, IBS, IBD, and Celiac disease patients using an immunoassay(i.e., ELISA). As shown in FIG. 10, quartile analysis revealed that NGALlevels were elevated in IBS samples relative to normal samples. Thus,NGAL can advantageously discriminate between IBS and normal patientsamples.

Example 6 MMP-9 Discriminates Between IBS and IBD Patient Samples

This example illustrates that determining the presence or level of MMP-9is useful for classifying a patient sample as an IBS sample, e.g., byruling in IBS or ruling out IBD. The concentration of MMP-9 was measuredin samples from normal, GI control, IBS, and IBD patients using animmunoassay (i.e., ELISA). As shown in FIG. 11, quartile analysisrevealed that MMP-9 levels were lower in IBS samples relative to IBDsamples. Thus, MMP-9 can advantageously discriminate between IBS and IBDpatient samples.

Example 7 NGAL/MMP-9 Complex Discriminates Between IBS and IBD PatientSamples

This example illustrates that determining the presence or level of acomplex of NGAL and MMP-9 (i.e., NGAL/MMP-9 complex) is useful forclassifying a patient sample as an IBS sample, e.g., by ruling in IBS orruling out IBD. The concentration of NGAL/MMP-9 complex was measured insamples from normal, IBS, and IBD patients using an immunoassay (i.e.,ELISA). As shown in FIG. 12, quartile analysis revealed that NGAL/MMP-9complex levels were lower in IBS samples relative to IBD samples. Thus,the NGAL/MMP-9 complex can advantageously discriminate between IBS andIBD patient samples.

Example 8 Substance P Discriminates Between IBS and Normal PatientSamples

This example illustrates that determining the presence or level ofSubstance P is useful for classifying a patient sample as an IBS sample,e.g., by ruling in IBS. The concentration of Substance P was measured insamples from normal, IBS, IBD (i.e., CD, UC), and Celiac diseasepatients using an immunoassay (i.e., ELISA). As shown in FIG. 13,quartile analysis revealed that Substance P levels were elevated in IBSsamples relative to normal samples. Thus, Substance P can advantageouslydiscriminate between IBS and normal patient samples.

Example 9 Cumulative Percent Histogram Analysis

FIG. 14 shows a cumulative percent histogram analysis using lactoferrinas a non-limiting example based on the frequency of samples at a rangeof lactoferrin concentrations in serum. These values can be plotted as astandard bar graph histogram (grey bars) displaying frequency versusconcentration. Each frequency divided by the total number of samplesprovides the percent frequency for that range, normalized for samplingpopulation size. The percent frequency for each successive range addedto the sum of lower ranges is the cumulative percent frequency, which isplotted to generate a curve culminating at 100 percent at the maximumlactoferrin concentration. The cumulative frequency curve for eachpatient population is then combined in a single graph to allow moreintuitive visualization of the measured differences between thedifferent populations. The further a particular curve is from anothercurve, the greater the likelihood that the patients can be accuratelyassigned to one of the two populations.

Example 10 Combinatorial Statistical Algorithm for Predicting IBS

Samples

Serum samples from 2,357 patients were obtained retrospectively frommultiple centers (Table 2). Diagnoses were provided for all samples bythe Principal Investigator at each site following biopsies and/orcolonoscopy results. Approximately 1 ml samples were drawn into SST orserum separators at the sites. The tubes were spun and frozen at −70° C.until shipment. Samples were shipped with cold packs and upon receiptwere spun again and frozen at −70° C. until testing. TABLE 2 Centersused to obtain samples for study cohort, N = 2,357. Location No. ofpatients CA 402 (HC + IBD) Toronto, Canada 1,287 (HC + IBD) Herestraat,Belgium 319 (HC + IBD) Bethesda, MD 163 (IBS) New York, NY 31 (IBS)Boston, MA 59 (IBS) Chicago, IL 60 (IBS) Lebanon, NH 36 (IBS)IBS = Irritable Bowel Syndrome, IBD = Inflammatory Bowel Disease, HC =Healthy Controls. Not all IBD samples were used in the development ofthe test.Assays

Serum levels of ANCA, ASCA-G, anti-Omp-C antibodies, anti-Cbirlantibodies, and IL-8 were carried out using an ELISA or animmunofluorescence assay. The analytical performance of these assays haspreviously been validated. IL-8 levels were measured with a commercialELISA kit (Invitrogen).

Statistical Analyses

In this study, a novel approach was developed that uses two differentlearning statistical classifiers (e.g., random forests (RF) andartificial neural networks (ANN)) to predict IBS based upon the levelsand/or presence of a panel of serological markers. These learningstatistical classifiers use multivariate statistical methods like, forexample, multilayer perceptrons with feed forward Back Propagation, thatcan adapt to complex data and make decisions based strictly on the datapresented, without the constraints of regular statistical classifiers.In particular, a combinatorial approach that makes use of multiplediscriminant functions by analyzing marker levels with more than onelearning statistical classifier was created to further improve thesensitivity and specificity of the diagnostic test. One preferred methodis a combination of RF and ANN applied in tandem. Overall accuracy wasused to determine the clinical performance of the test in the validationpopulation.

Marker values from more than 2,000 patient samples were first split intotraining, testing, and validating cohorts (Table 3). Different patientsamples were used for training, testing, and for validation purposes.TABLE 3 Sample sets used to create diagnostic algorithms. Number of IBSNormal/IBS/ Samples Prevalence IBD Training Cohort 263 30% 108/79/76Testing Cohort 100 35% 36/35/29 Total: Training & Testing 363 31%144/114/105 Validating Cohort 200 28% 86/55/59Normal and IBD patients were used as non-IBS controls. IBS samples werea mix of D-IBS, C-IBS and A-IBS.Random Forests

The antibody levels from each of the 4 ELISA assays (predictors) and thediagnosis (0=Non-IBS, 1=IBS, 2=IBD, Dependent Variable) from a cohort of263 patient samples (30% IBS prevalence, training set, illustrated inTable 2) were used as input for the RF software module. Multiple RFmodels were created and analyzed for accuracy of IBS prediction usingthe test cohort. The best predictive RF models were selected and testedfor accuracy of IBS prediction using data from the validation cohort.

Several RF models were used to predict IBS, IBD, or non-IBS from thetraining set. The output data were used as input for training neuralnetworks. The outputs from the RF software module included a predictionvalue (i.e., 0 [non-IBS], 1 [IBS], or 2 [IBD]) and 3 probability orconfidence values (one for each prediction). The three probabilityvalues were used together with the levels of the markers, as predictorvalues for further statistical analysis using ANN. A schematicrepresentation of data processing is illustrated in FIG. 15. FIG. 16illustrates the data set obtained using the model of FIG. 15.

Artificial Neural Networks

The values of the markers and the probabilities of non-IBS, IBS, and IBDpredictions obtained from the RF model (Salford Systems; San Diego,Calif.) were used as predictors and the diagnosis as a dependentvariable to create multiple ANN with the use of the neural networkssoftware. The Intelligent Problem Solver module of the neural networkssoftware package (Statistica; StatSoft, Inc.; Tulsa, Okla.) was used tocreate ANN models in a feed-forward, backpropagation, and classificationmode with the training cohort. More than 1,000 ANN were created usingthe input from various RF models. The best models were selected based onthe lowest error of IBS prediction on the test dataset.

A diagram of an ANN is shown in FIG. 17. This model is composed of aMulti-level Perceptron containing 1 hidden layer with 10 neurons. Therelative activation of the neuron is identified by its color.

Algorithm Validation and Accuracy of Prediction

The selected algorithm was then validated with a cohort of samples thathad not been used in the training and testing sets (i.e., the validationset). The data obtained from this test was used to calculate allaccuracy parameters for the algorithm.

Additionally, final validation and calculation of accuracy was performedon data from a sample cohort non-overlapping with the training andtesting sets. The 2×2 confusion matrix (Table 4) shows the algorithmprediction results on the validation cohort. TABLE 4 2 × 2 confusionmatrix. 2 × 2 Matrix of Algorithm Prediction on the Validation CohortNon-IBS IBS Non-IBS 91 8 IBS 7 125

The algorithm prediction accuracy for IBS is shown in Table 5. TABLE 5Clinical performance of algorithm in the prediction of IBS. Accuracy ofIBS Prediction of Hybrid Model Tested in Validation Cohort TP 187 IBSSensitivity 91.2% FN 18 IBS Specificity 86.8% FP 19 IBS PPV 90.8% TN 125IBS NPV 87.4%TP = True positives, FN = False negatives, FP = False positives, TN =True negatives, PPV = Positive predictive value, NPV = Negativepredictive value.Prediction accuracy was calculated using the algorithm on the validationset.

The sensitivity and specificity of IBS prediction were about 91% andabout 87%, respectively. IBS PPV and NPV were about 91% and about 87%,respectively. Accurate identification of IBS was revealed bysensitivities and specificities near or above 90%. Overall accuracy ofprediction was calculated as shown in Table 6. The hybrid RF/ANN modelpredicted IBS with a high level of accuracy. TABLE 6 Overall predictionaccuracy. Correctly Predicted/ Total Number % Correct Hybrid ModelDiagnosed Prediction Overall Assay Accuracy 159/200 80%Percent correct prediction was calculated as follows: Accuracy = IBSTP + IBD TP + TN/Total number of samples tested.

Example 11 Random Forest Statistical Algorithm for Predicting IBS

Dataset

A total of 939 patient samples were analyzed using a random forest (RF)statistical algorithm. The samples were split into training, testing,and validating cohorts as follows: (1) 739 training and testing samples(Table 7); and (2) 200 validating samples. Different patient sampleswere used for training, testing, and for validation purposes. TABLE 7Composition of the training and testing cohort. Composition ofTrain/Test Cohort Normal 257 35% IBS 152 21% Celiac 34  5% CD 154 21% UC142 19% Total 739Assays

Serum levels of IL-8, lactoferrin, ANCA, ASCA-G, and anti-Omp-Cantibodies were carried out using an ELISA as described above.

Study Approach

In this study, a novel approach was developed that uses a singlelearning statistical classifier (i.e., random forests) to predict IBSbased upon the levels and/or presence of a panel of serological markers.The antibody levels from each of the ELISA assays (predictors; Table 8)and the diagnosis from the train/test cohort of patient samples wereused as input for the RF software module (Salford Systems; San Diego,Calif.). Multiple RF models were created and analyzed for accuracy ofIBS prediction using the train/test cohort. The best predictive RFmodels were selected and tested for accuracy of IBS prediction usingdata from the validation cohort. TABLE 8 Predictive importance of eachof the diagnostic markers analyzed. Marker Score IL-8 100.0 Lactoferrin34.14 ANCA 19.15 Anti-Omp-C Antibodies 7.18 ASCA-G 6.14Values are normalized to IL-8.Algorithm Validation and Accuracy of Prediction

The selected RF algorithm was then validated with a cohort of samplesthat had not been used in the training and testing sets (i.e., thevalidation set). The data obtained from this test was used to calculateall accuracy parameters for the algorithm.

The RF algorithm prediction accuracy for IBS is shown in Table 9. TABLE9 Clinical performance of the RF algorithm in the prediction of IBS.Non-IBS IBS Cases Total Cases Percent Correct (N = 135) (N = 65) Non-IBS151 84.7 (Specificity) 128 23 IBS 49 85.7 (Sensitivity) 7 42

The sensitivity and specificity of IBS prediction were 85.7% and 84.7%,respectively. Accurate identification of IBS was revealed bysensitivities and specificities near or above 85%. The RF modelpredicted IBS with a high level of accuracy.

FIG. 18 illustrates the distribution of IBS and non-IBS samples beforeand after modeling with a RF algorithm using serum levels of IL-8, EGF,ANCA, and ASCA-G.

Example 12 Classification Tree Statistical Algorithm for Predicting IBS

Dataset

Approximately 430 cases are analyzed using a classification treestatistical algorithm. These cases can have serological markerinformation for IL-8, ANCA ELISA, anti-Omp-C antibodies, ASCA-A, ASCA-G,anti-Cbirl antibodies, pANCA, and/or lactoferrin.

Study Approach

In this study, a novel approach is developed that uses a single learningstatistical classifier (i.e., classification trees) to predict IBS basedupon the levels and/or presence of a panel of serological markers. Inorder to generate robust estimates of the efficacy of eachclassification method, a simulation with 500 iterations is performed.For each iteration, the data is divided into a training set and avalidation set. Each time, 80% of the observations are randomly assignedto the training set and 20% of the observations are randomly assigned tothe validation set. Using the training set, classification models arebuilt using classification trees.

Classification Trees

Classification trees are constructed by repeated binary splits ofsubsets of the data, beginning with the complete dataset. Each time abinary split is performed, there is an attempt to create descendentsubsets that are “purer,” or more homogeneous, than the parent subset.This is done by computationally finding a split that achieves thelargest decrease in the average impurity of the descendent subsets.Impurity is usually defined in operational terms by one of threemetrics:

-   -   1) Misclassification rate;    -   2) Gini index; or    -   3) Entropy (deviance).

Though minimizing the misclassification rate is the overall goal, it isconsidered a poor criterion for the split search because it producesonly a one-step optimization. The Gini index and entropy criterionproduce similar results for two-class problems (Hastie et al., TheElements of Statistical Learning, New York; Springer (2001)). The nodescreated by each binary split are recursively split until one of thefollowing three conditions becomes true:

-   -   1) All cases in the node are of the same observed class (i.e.,        the impurity is equal to zero);    -   2) The node only contains observations that have identical        measurements (i.e., there is no way to split the remaining        observations); or,    -   3) The node is small, typically 1 to 5 observations.

Once a terminal point has been reached for every node, the tree ispruned upward. This procedure creates a sequence of smaller and smallertrees. The overall impurity of each of these trees can be measured andthe one with the smallest total impurity selected. This may be regardedas the “best” classification tree (Breiman et al., Classification andRegression Trees, Wadsworth; Belmont, Calif. (1984)).

Once the “best” tree is selected, the predicted class of each of theterminal nodes is determined by a simple majority “vote” of eachobservation in the node. In order to classify a new case, the newobservation is simply sent down the tree. The predicted class of the newobservation is the predicted class of the terminal node in which it isplaced. Further discussion and examples may be found, e.g., in Hastie etal., supra; and Venables et al., Modern Applied Statistics with S-Plus,4th edition; New York; Springer (2002).

FIG. 19 shows a three node classification tree for classifying a sampleas an IBS sample or non-IBS sample based upon the levels of IL-8,lactoferrin, and ANCA ELISA. This classification tree provides anapproximate overall correct classification rate of 87.6%.

Example 13 Questionnaire for Identifying the Presence or Severity ofSymptoms Associated with IBS

This example illustrates a questionnaire that is useful for identifyingthe presence or severity of one or more IBS-related symptoms in anindividual. The questionnaire can be completed by the individual at theclinic or physician's office, or can be brought home and submitted whenthe individual returns to the clinic or physician's office, e.g., tohave his or her blood drawn.

In some embodiments, the questionnaire comprises a first sectioncontaining a set of questions asking the individual to provide answersregarding the presence or severity of one or more symptoms associatedwith IBS. The questionnaire generally includes questions directed toidentifying the presence, severity, frequency, and/or duration ofIBS-related symptoms such as chest pain, chest discomfort, heartburn,uncomfortable fullness after having a regular-sized meal, inability tofinish a regular-sized meal, abdominal pain, abdominal discomfort,constipation, diarrhea, bloating, and/or abdominal distension.

In certain instances, the first section of the questionnaire includesall or a subset of the questions from a questionnaire developed by theRome Foundation Board based on the Rome III criteria, available athttp://www.romecriteria.org/questionnaires/. For example, thequestionnaire can include all or a subset of the 93 questions set forthon pages 920-936 of the Rome III Diagnostic Questionnaire for the AdultFunctional GI Disorders (Appendix C), available athttp://www.romecriteria.org/pdfs/AdultFunctGIQ.pdf. Preferably, thefirst section of the questionnaire contains 16 of the 93 questions setforth in the Rome III Diagnostic Questionnaire (see, Table 10).Alternatively, the first section of the questionnaire can contain asubset (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15) ofthe 16 questions shown in Table 10. As a non-limiting example, thefollowing 10 questions set forth in Table 10 can be included in thequestionnaire: Question Nos. 2, 3, 5, 6, 9, 10, 11, 13, 15, and 16. Oneskilled in the art will appreciate that the first section of thequestionnaire can comprise questions similar to the questions shown inTable 10 regarding pain, discomfort, and/or changes in stoolconsistency. TABLE 10 Exemplary first section of a questionnaire foridentifying the presence or severity of IBS-related symptoms. 1. In thelast 3 months, {circle around (0)} Never how often did you have {circlearound (1)} Less than one day a month pain or discomfort in the {circlearound (2)} One day a month middle of your chest {circle around (3)} Twoto three days a month (not related to heart {circle around (4)} One daya week problems)? {circle around (5)} More than one day a week {circlearound (6)} Every day 2. In the last 3 months, {circle around (0)} Neverhow often did you have {circle around (1)} Less than one day a monthheartburn (a burning {circle around (2)} One day a month discomfort orburning {circle around (3)} Two to three days a month pain in yourchest)? {circle around (4)} One day a week {circle around (5)} More thanone day a week {circle around (6)} Every day 3. In the last 3 months,{circle around (0)} Never → how often did you feel {circle around (1)}Less than one day a month uncomfortably full after {circle around (2)}One day a month a regular-sized meal? {circle around (3)} Two to threedays a month {circle around (4)} One day a week {circle around (5)} Morethan one day a week {circle around (6)} Every day 4. In the last 3months, {circle around (0)} Never → how often were you {circle around(1)} Less than one day a month unable to finish a {circle around (2)}One day a month regular size meal? {circle around (3)} Two to three daysa month {circle around (4)} One day a week {circle around (5)} More thanone day a week {circle around (6)} Every day 5. In the last 3 months,{circle around (0)} Never → how often did you have {circle around (1)}Less than one day a month pain or burning in the {circle around (2)} Oneday a month middle of your {circle around (3)} Two to three days a monthabdomen, above your {circle around (4)} One day a week belly button butnot in {circle around (5)} More than one day a week your chest? {circlearound (6)} Every day 6. In the last 3 months, {circle around (0)} Never→ how often did you have {circle around (1)} Less than one day a monthdiscomfort or pain {circle around (2)} One day a month anywhere in your{circle around (3)} Two to three days a month abdomen? {circle around(4)} One day a week {circle around (5)} More than one day a week {circlearound (6)} Every day 7. In the last 3 months, {circle around (0)} Neveror rarely how often did you have {circle around (1)} Sometimes fewerthan three bowel {circle around (2)} Often movements (0-2) a {circlearound (3)} Most of the time week? {circle around (4)} Always 8. In thelast 3 months, {circle around (0)} Never or rarely how often did youhave {circle around (1)} Sometimes (25% of the time) hard or lumpystools? {circle around (2)} Often (50% of the time) {circle around (3)}Most of the time (75% of the time) {circle around (4)} Always 9. In thelast 3 months, {circle around (0)} Never or rarely how often did youstrain {circle around (1)} Sometimes during bowel {circle around (2)}Often movements? {circle around (3)} Most of the time {circle around(4)} Always 10. In the last 3 months, {circle around (0)} Never orrarely how often did you have {circle around (1)} Sometimes a feeling ofincomplete {circle around (2)} Often emptying after bowel {circle around(3)} Most of the time movements? {circle around (4)} Always 11. In thelast 3 months, {circle around (0)} Never or rarely how often did youhave {circle around (1)} Sometimes a sensation that the stool {circlearound (2)} Often could not be passed, {circle around (3)} Most of thetime (i.e., blocked), when {circle around (4)} Always having a bowelmovement? 12. In the last 3 months, {circle around (0)} Never or rarelyhow often did you press {circle around (1)} Sometimes on or around your{circle around (2)} Often bottom or remove stool {circle around (3)}Most of the time in order to complete a {circle around (4)} Always bowelmovement? 13. Did any of the {circle around (0)} No symptoms of {circlearound (1)} Yes constipation listed in questions 27-32 above begin morethan 6 months ago? 14. In the last 3 months, {circle around (0)} Neveror rarely → how often did you have {circle around (1)} Sometimes (25% ofthe time) loose, mushy or watery {circle around (2)} Often (50% of thetime) stools? {circle around (3)} Most of the time (75% of the time){circle around (4)} Always 15. In the last 3 months, {circle around (0)}Never → how often did you have {circle around (1)} Less than one day amonth bloating or distension? {circle around (2)} One day a month{circle around (3)} Two to three days a month {circle around (4)} Oneday a week {circle around (5)} More than one day a week {circle around(6)} Everyday 16. Did your symptoms of {circle around (0)} No bloatingor distention {circle around (1)} Yes begin more than 6 months ago?

In other embodiments, the questionnaire comprises a second sectioncontaining a set of questions asking the individual to provide answersregarding the presence or severity of negative thoughts or feelingsassociated with having IBS-related pain or discomfort. For example, thequestionnaire can include questions directed to identifying thepresence, severity, frequency, and/or duration of anxiety, fear,nervousness, concern, apprehension, worry, stress, depression,hopelessness, despair, pessimism, doubt, and/or negativity when theindividual is experiencing pain or discomfort associated with one ormore symptoms of IBS.

In certain instances, the second section of the questionnaire includesall or a subset of the questions from a questionnaire described inSullivan et al., The Pain Catastrophizing Scale: Development andValidation, Psychol. Assess., 7:524-532 (1995). For example, thequestionnaire can include a set of questions to be answered by anindividual according to a Pain Catastrophizing Scale (PCS), whichindicates the degree to which the individual has certain negativethoughts and feelings when experiencing pain: 0=not at all; 1=to aslight degree; 2=to a moderate degree; 3=to a great degree; 4=all thetime. The second section of the questionnaire can contain 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or more questions or statementsrelated to identifying the presence or severity of negative thoughts orfeelings associated with having IBS-related pain or discomfort. As anon-limiting example, an individual can be asked to rate the degree towhich he or she has one or more of the following thoughts and feelingswhen experiencing pain: “I worry all the time about whether the painwill end”; “I feel I can't stand it anymore”; “I become afraid that thepain will get worse”; “I anxiously want the pain to go away”; and “Ikeep thinking about how much it hurts.” One skilled in the art willunderstand that the questionnaire can comprise similar questionsregarding negative thoughts or feelings associated with havingIBS-related pain or discomfort.

In some embodiments, the questionnaire includes only questions from thefirst section of the questionnaire or a subset thereof (see, e.g., Table10). In other embodiments, the questionnaire includes only questionsfrom the second section of the questionnaire or a subset thereof.

Upon completion of the questionnaire by the individual, the numberscorresponding to the answers to each question can be summed and theresulting value can be combined with the analysis of one or morediagnostic markers in a sample from the individual and processed usingthe statistical algorithms described herein to increase the accuracy ofpredicting IBS.

Alternatively, a “Yes” or “No” answer from the individual to thefollowing question: “Are you currently experiencing any symptoms?” canbe combined with the analysis of one or more of the biomarkers describedherein and processed using a single statistical algorithm or acombination of statistical algorithms to increase the accuracy ofpredicting IBS.

Example 14 Selection of Diagnostic Markers and Symptoms for PredictingIBS

This example illustrates techniques for the selection of features thatcan be included in the diagnostic marker and symptom profiles of thepresent invention for predicting IBS.

1. Introduction

The goal of classification is to take an input vector X and assign it toone or more of K distinct classes C_(j), where j is in the range (1 . .. K). (Bishop, Pattern Recognition and Machine Learning, Springer, p.179 (2006)). In the context of a diagnostic test algorithm, the inputvector may consist of a combination of quantitative measurements (e.g.,biomarkers), nominal variables (e.g., gender), and ordinal variables(e.g., symptom presence or severity from survey responses). Thesecomponents of the input vector may collectively be termed features. Theinput vector describes a patient for whom a diagnosis is desired. Theoutput of the model is the diagnosis, a categorical variable (e.g., abinary variable, where 0=healthy and 1=disease).

A diagnostic test involves specifying the features of the input vector,and the algorithm used to predict the classifications. While it ispossible to use a maximal model, in which all input features and theirinteractions are included, this is not preferred, for reasons of economyand parsimony (Crawley, Statistical Computing: An Introduction to DataAnalysis using S-Plus, Wiley, p. 211 (2002)). Economy suggests thatsince gathering inputs entails costs, the cost of obtaining an inputmust be weighed against its benefit. Parsimony suggests that simplermodels are preferable, and that inputs and/or terms which areinsignificant should not be included, in order to optimize the clarityand reliability of the test.

A number of techniques may be used to select the features of the inputvector which will be used in a diagnostic test. These techniques arediscussed in the following paragraphs. Some input selection techniquesare algorithm-independent, and may be used with any classificationalgorithm. Others are algorithm-specific. Examples of severalalgorithm-independent techniques, followed by techniques which arespecifically applicable to random forest, logistic regression, ordiscriminant analysis algorithms are provided.

2. Algorithm—Independent Techniques

In considering generally applicable techniques, two families ofapproaches are available: statistical and stepwise-exploratory. If theinput data fits certain assumptions (regarding normality and equality ofvariance), statistical techniques may be used, as described below.Stepwise methods may be used whether or not those assumptions are met bythe data.

2.1 Statistical Techniques

A number of classic standard tests may be used on features, bothindividually (univariate tests) and in groups (multivariate tests). Forexample, for quantitative biomarkers, the diagnostic classifications inthe input data lead to group means which can be compared using t-tests.This requires that two assumptions are valid: the variable is normallydistributed in each group; and the variance of the two groups are thesame (Petrie & Sabin, Medical Statistics at a Glance, 2nd ed., BlackwellPublishing, p. 52 (2005)). This test has a multivariate analog: in amultivariate comparison, Hotelling's T² test may be used (Flury, A FirstCourse in Multivariate Statistics, Springer-Verlag, p. 402 (1997)).

If the required assumptions are not met, a number of nonparametric testsare available, such as the Mann-Whitney Rank-Sum test, the Wilcoxon ranksum test, and the Kruskal-Wallis statistic for three or more groups(Glantz, Primer of Biostatistics, 4th ed., McGraw-Hill, Chapter 10(1997)).

For both the parametric and nonparametric tests, the results may be usedto suggest which biomarkers (or groups of features) do or do not havesignificantly different mean scores for the diagnostic groups.

2.2 Stepwise Methods

The following stepwise methods assume that an algorithm has been chosen(e.g., random forest, logistic regression), but these methods may beused with any algorithm, and they are in that sensealgorithm-independent. In the context of the selected algorithm, it isdesirable to choose a set of features from those available in the inputvector. In order to use an exploratory technique, a scoring metric and asearch method must be defined.

2.2.1 Scoring Metric

The first step is to choose a metric by which competing feature sets maybe scored. One possible metric is accuracy, the percentage of correctpredictions made by the classifier (both true positive and truenegative). Alternatively, the scoring metric may be defined in terms ofsensitivity (the percentage of individuals with disease who areclassified as having the disease) and/or specificity (the percentage ofindividuals without disease who are classified as not having thedisease) (Fisher & Belle, Biostatistics: A Methodology for the HealthSciences, Wiley-Interscience, p. 206 (1993)). Less commonly, the metricmay also involve positive predictive value (ppv, the percentage ofindividuals with a positive test who have the disease) and negativepredictive value (npv, the percentage of individuals with a negativetest who do not have the disease).

The following is a list of available metrics: accuracy; sensitivity(alone); specificity (alone); the arithmetic mean of sensitivity andspecificity; the geometric mean of sensitivity and specificity; theminimum of sensitivity and specificity; and the maximum of sensitivityand specificity. A similar set of metrics may be used with ppv and npv:ppv/npv alone; arithmetic mean; geometric mean; max; and min. It is alsopossible to define metrics which combine sensitivity, specificity, ppv,and npv (e.g., the arithmetic mean of those four values). It is alsopossible to define specific penalties for false positives and falsenegatives, in which case the score is to be minimized rather thanmaximized.

2.2.2 Search Method

For any of the scoring metrics defined above, it is possible to evaluateany algorithm (including random forest, logistic regression,discriminant analysis, and others) by exhaustively enumerating everypossible subset of features in the input vector. In cases where this isunacceptably computationally intensive, it is possible to conduct astepwise search in which individual features are added (a forwardsearch) or removed (a backwards search) one by one, in a series ofrounds (Petrie & Sabin, Medical Statistics at a Glance, 2nd ed.,Blackwell Publishing, p. 89 (2005)).

In a forward search, features (e.g., biomarkers, symptoms, etc.) areadded one by one, in rounds. In the first round, an input vectorconsisting of one feature is evaluated on the training data, and thebest feature (defined by the metric described above) is identified. Inthe second round, a new set of input features is constructed andevaluated. Each set has two features, one of which is the “best” featurefrom the first round of evaluation. The best pair of features from thesecond round is chosen, and becomes the basis for the third round, inwhich all input vectors have three features, two of which are the onesidentified in the second round, and so forth. This procedure is carriedout iteratively, with the number of rounds equal to the number ofpossible features in the input vector. At the conclusion, the best inputvector (i.e., set of features), as defined by the metric, is selected.

A backward search is similar, but follows a process of modelsimplification rather than model expansion (Crawley, Statistics: AnIntroduction Using R, Wiley, p. 105 (2005)). The starting point is theinput vector with a complete set of features. In each round, oneparameter is chosen for deletion, as evaluated by the metric describedabove.

In addition to exhaustive forward and backward searches, it is possibleto search stochastically. One method is to randomly generate a set offeatures, which are used as seeds. Each seed may then be evaluated bothforward and backward, and the best resulting set of inputs may be used.An alternative method is to carry out multiple forward and/or backwardsearches, but in each round, rather than deterministically choosing thebest feature addition or deletion, probabilistically choosing thefeature to include or delete by a formula which monotonicallydecreases/increases the probability of addition/deletion based on theranking in the last round.

3. Algorithm-Specific Techniques

Having discussed methods for feature selection which are applicable toany algorithm, this section describes methods which are specific toparticular algorithms. Three representative algorithms are discussed:random forests; logistic regression; and discriminant analysis.

3.1 Random Forests

For random forests, two metrics are available to describe the importanceof features: permutation importance (Strobl et al., BMC Bioinformatics,8:25 (2007)) and gini importance (Breiman et al., Classification andRegression Trees, Chapman & Hall/CRC, p. 146 (1984)).

For permutation importance, the idea is to compare the scoring of a fullforest to the scoring produced by a forest in which the input values forone feature have been scrambled. Intuitively, the more important thefeature, the more the scoring will be reduced if the values of thatfeature have been randomly permuted. The decrease in score is thepermutation importance; by evaluating all the features in this way,their importance may be ranked.

For gini importance, the idea is to take a weighted mean of theindividual trees' improvement in the “gini gain” splitting criterionproduced by each feature. Every time a split of a node is made on acertain feature, the gini impurity criterion for the two descendentnodes is less than the parent node. Adding up the gini decreases foreach individual feature over all trees in the forest gives a measure offeature importance.

3.2 Logistic Regression

Logistic regression is used in cases where the dependent variable (e.g.,diagnosis) is categorical/nominal. (Agresti, An Introduction toCategorical Data Analysis, 2nd ed., Wiley-Interscience, Chapter 4(2007)). An extensive literature describes techniques for feature/modelselection in multiple regression (Maindonald & Braun, Data Analysis andGraphics Using R, 2nd ed., Cambridge University Press, Chapter 6(2003)).

In logistic and other types of regression, the significance ofindividual features may be assessed by testing the hypothesis that thecorresponding regression coefficient is zero (Kachigan, MultivariateStatistical Analysis, A Conceptual Introduction, 2nd ed., Radius Press,p. 178 (1991)). It is also possible to assess a group of features on thebasis of a deletion test, e.g., using an F test to assess thesignificance of the increase in deviance that results when a given termis removed from a regression model (Crawley, Statistics: An IntroductionUsing R, Wiley, p. 103 (2005); Devore, Probability and Statistics forEngineering and the Sciences, 4th ed., Brooks/Cole, p. 560 (1995)).

3.3 Discriminant Analysis

Discriminant analysis describes a set of techniques in which theparametric form of a discriminant function is assumed, and theparameters of the discriminant function are fitted. This is in contrastto techniques in which the parametric form of the underlying probabilitydensities are assumed and fitted, rather than the discriminant function.The canonical example in this family of techniques is Fisher's lineardiscriminant analysis (LDA); related techniques and extensions includequadratic discriminant analysis (QDA), regularized discriminantanalysis, mixture discriminant analysis, and others (Venables & Ripley,Modern Applied Statistics with S, 4th ed., Springer, Chapter 12 (2002)).Feature selection for LDA is discussed below; the discussion is alsoapplicable to related techniques in this family.

In LDA, the coefficients of the linear discriminant are chosen tomaximize the class separation, as measured by the ratio of thebetween-class variance and the within-class variance (Everitt & Dunn,Applied Multivariate Data Analysis, 2nd ed., Oxford University Press, p.253 (2001)). In this context, the redundancy of features may be formallyinferred (Flury, A First Course in Multivariate Statistics,Springer-Verlag, Sections 5.6 and 6.5 (1997)). This is done by testingthe hypothesis that the relevant discriminant function coefficients arezero. By inference on the discriminant function coefficients, it ispossible to construct tests of sufficiency/redundancy for possiblegroups of features.

3.4 Other Algorithms

A large number of other algorithms are available for diagnosticclassification, including neural networks, support vector machines, CART(classification and regression trees), unsupervised clustering (k-means,Gaussian mixtures), k-nearest neighbors, and many others. For many ofthese algorithms, algorithm-specific techniques are available forevaluating and selecting features. In addition, some techniques focus onfeature extraction (choosing a smaller number of features which may belinear or nonlinear combinations of the available features). Thesetechniques include principal component analysis, independent componentanalysis, factor analysis, and other variations (Duda et al., PatternClassification, 2nd ed., Wiley-Interscience, p. 568 (2001)).

Example 15 Symptom Profile for Predicting IBS

This example illustrates techniques for use of a questionnaire toimprove accuracy of an IBS diagnostic prediction algorithm.

In certain instances, identifying patients with IBS is more accuratelypredicted with the use of one or more questions as predictors to createan alternative algorithm or further input to provide added sensitivityand specificity.

In certain instances, questions were generated such as “Are youcurrently experiencing any symptoms?,” while others were extracted fromknown questionnaires such as Rome II, Rome III, the Pain CatastrophizingScale (Sullivan et al., The Pain Catastrophizing Scale: Development andValidation, Psychol. Assess., 7:524-532 (1995)), and the like. Somequestions had nominal answers (rates degree of some occurrence), whileothers were categorical (binary). In the Rome III questions, the nominalvalue of all answers from a patient were added to create a single scorethat was considered a simplified “disease severity” score. In certainembodiments, inclusion of this score together with the biomarker levelsimproved both the sensitivity and specificity of an algorithm.

In one embodiment, the score of each question (e.g., 0-4) was used asinput (predictor) together with all biomarkers. Models were then createdusing Random Forests and Neural Networks. Both Random Forests and NeuralNetworks have the capability to determine the most significant questionsthat improve the accuracy of algorithm prediction. After having selectedthe best questions, one score was used to predict “disease severity,” orlevel of Catastrophizing, by summing the values of each question for aparticular patient. The data that included the questionnaire scores wereused to train algorithms using Random Forests, Neural Networks and otherstatistical classifiers. The questions from Rome II, Rome III, and thePain Catastrophizing Scale improved the accuracy of prediction when usedin combination with multiple biomarkers to identify patients with IBS.In addition, a single question, “Are you currently experiencing anysymptoms?” (yes or no), was in some instances as important as the scoresum of the answers to the questions in the questionnaire.

Table 11 shows that a symptom profile can improve the accuracy of IBSprediction. With the inclusion of various data from questionnaires asinput predictors, specificity and sensitivity can both be improved.TABLE 11 Improvement of accuracy of IBS prediction by inclusion ofvarious questionnaires as input predictors. SEVERITY SCALE X XCATASTROPHIZING X X SCALE CURRENT SYMPTOMS X X CBIR1 X X X X X ANCAELISA X X X X X EGF X X X X X ASCA-IgG X X X X X ASCA-IgA X X X X X AGEX X X X X ANTI-OMPC X X X X X IL-8 X X X X X LACTOFERRIN X X X X X ANTI-X X X X X TRANSGLUTAMINASE SENSITIVITY 69% 76% 70% 73% 69% SPECIFICITY44% 89% 87% 63% 94%

As the data in Table 11 shows, the specificity is increased with the useof questionnaire data and on average, sensitivity is also increased.Sensitivity is the probability of a positive test among patients withIBS, whereas specificity is the probability of a negative test amongpatients without IBS.

Although the foregoing invention has been described in some detail byway of illustration and example for purposes of clarity ofunderstanding, one of skill in the art will appreciate that certainchanges and modifications may be practiced within the scope of theappended claims. In addition, each reference provided herein isincorporated by reference in its entirety to the same extent as if eachreference was individually incorporated by reference.

1. A method for classifying whether a sample from an individual isassociated with irritable bowel syndrome (IBS), said method comprising:(a) determining a diagnostic marker profile by detecting the presence orlevel of at least one diagnostic marker selected from the groupconsisting of a cytokine, growth factor, anti-neutrophil antibody,anti-Saccharomyces cerevisiae antibody (ASCA), antimicrobial antibody,lactoferrin, anti-tissue transglutaminase (tTG) antibody, lipocalin,matrix metalloproteinase (MMP), tissue inhibitor of metalloproteinase(TIMP), alpha-globulin, actin-severing protein, S100 protein,fibrinopeptide, calcitonin gene-related peptide (CGRP), tachykinin,ghrelin, neurotensin, corticotropin-releasing hormone, and combinationsthereof in said sample; and (b) classifying said sample as an IBS sampleor non-IBS sample using an algorithm based upon said diagnostic markerprofile.
 2. The method of claim 1, wherein said cytokine is selectedfrom the group consisting of IL-8, IL-1β, TNF-related weak inducer ofapoptosis (TWEAK), leptin, osteoprotegerin (OPG), MIP-3β, GROα,CXCL4/PF-4, CXCL7/NAP-2, and combinations thereof.
 3. The method ofclaim 1, wherein said growth factor is selected from the groupconsisting of epidermal growth factor (EGF), vascular endothelial growthfactor (VEGF), pigment epithelium-derived factor (PEDF), brain-derivedneurotrophic factor (BDNF), amphiregulin (SDGF), and combinationsthereof.
 4. The method of claim 1, wherein said anti-neutrophil antibodyis selected from the group consisting of an anti-neutrophil cytoplasmicantibody (ANCA), perinuclear anti-neutrophil cytoplasmic antibody(pANCA), and combinations thereof.
 5. The method of claim 1, whereinsaid ASCA is selected from the group consisting of ASCA-IgA, ASCA-IgG,and combinations thereof.
 6. The method of claim 1, wherein saidantimicrobial antibody is selected from the group consisting of ananti-outer membrane protein C (anti-OmpC) antibody, anti-flagellinantibody, anti-I2 antibody, and combinations thereof.
 7. The method ofclaim 1, wherein said lipocalin is selected from the group consisting ofneutrophil gelatinase-associated lipocalin (NGAL), an NGAL/MMP-9complex, and combinations thereof.
 8. The method of claim 1, whereinsaid MMP is MMP-9.
 9. The method of claim 1, wherein said TIMP isTIMP-1.
 10. The method of claim 1, wherein said alpha-globulin isselected from the group consisting of alpha-2-macroglobulin,haptoglobin, orosomucoid, and combinations thereof.
 11. The method ofclaim 1, wherein said actin-severing protein is gelsolin.
 12. The methodof claim 1, wherein said S100 protein is calgranulin.
 13. The method ofclaim 1, wherein said fibrinopeptide is fibrinopeptide A (FIBA).
 14. Themethod of claim 1, wherein said diagnostic marker profile is determinedby detecting the presence or level of at least two, three, four, five,or six diagnostic markers.
 15. The method of claim 1, wherein thepresence or level of said at least one diagnostic marker is detectedusing a hybridization assay, amplification-based assay, immunoassay, orimmunohistochemical assay.
 16. The method of claim 1, wherein saidmethod comprises determining said diagnostic marker profile incombination with a symptom profile, wherein said symptom profile isdetermined by identifying the presence or severity of at least onesymptom in said individual; and classifying said sample as an IBS sampleor non-IBS sample using an algorithm based upon said diagnostic markerprofile and said symptom profile.
 17. The method of claim 16, whereinsaid at least one symptom is selected from the group consisting of chestpain, chest discomfort, heartburn, uncomfortable fullness after having aregular-sized meal, inability to finish a regular-sized meal, abdominalpain, abdominal discomfort, constipation, diarrhea, bloating, abdominaldistension, negative thoughts or feelings associated with having pain ordiscomfort, and combinations thereof.
 18. The method of claim 16,wherein the presence or severity of said at least one symptom isidentified using a questionnaire.
 19. The method of claim 18, whereinsaid questionnaire is selected from the group consisting of a set ofquestions asking said individual about the presence or severity of saidat least one symptom, a set of questions asking said individual aboutthe presence or severity of negative thoughts or feelings associatedwith having pain or discomfort, and combinations thereof.
 20. The methodof claim 16, wherein the presence or severity of said at least onesymptom is identified by asking said individual whether said individualis currently experiencing any symptoms.
 21. The method of claim 16,wherein said symptom profile is determined by identifying the presenceor severity of at least two, three, four, five, or six symptoms.
 22. Themethod of claim 1, wherein said sample is selected from the groupconsisting of serum, plasma, whole blood, and stool.
 23. The method ofclaim 1, wherein said algorithm comprises a statistical algorithm. 24.The method of claim 23, wherein said statistical algorithm comprises alearning statistical classifier system.
 25. The method of claim 24,wherein said learning statistical classifier system is selected from thegroup consisting of a random forest, classification and regression tree,boosted tree, neural network, support vector machine, generalchi-squared automatic interaction detector model, interactive tree,multiadaptive regression spline, machine learning classifier, andcombinations thereof.
 26. The method of claim 23, wherein saidstatistical algorithm comprises a single learning statistical classifiersystem.
 27. The method of claim 23, wherein said statistical algorithmcomprises a combination of at least two learning statistical classifiersystems.
 28. The method of claim 1, wherein said method furthercomprises classifying said non-IBS sample as a normal, inflammatorybowel disease (IBD), or non-IBD sample.
 29. The method of claim 1,wherein said method further comprises sending the results from saidclassification to a clinician.
 30. The method of claim 1, wherein saidmethod further provides a diagnosis in the form of a probability thatsaid individual has IBS.
 31. The method of claim 1, wherein said methodfurther comprises classifying said IBS sample as an IBS-constipation(IBS-C), IBS-diarrhea (IBS-D), IBS-mixed (IBS-M), IBS-alternating(IBS-A), or post-infectious IBS (IBS-PI) sample.
 32. The method of claim1, wherein said method further comprises ruling out intestinalinflammation.
 33. A method for monitoring the progression or regressionof irritable bowel syndrome (IBS) in an individual, said methodcomprising: (a) determining a diagnostic marker profile by detecting thepresence or level of at least one diagnostic marker selected from thegroup consisting of a cytokine, growth factor, anti-neutrophil antibody,anti-Saccharomyces cerevisiae antibody (ASCA), antimicrobial antibody,lactoferrin, anti-tissue transglutaminase (tTG) antibody, lipocalin,matrix metalloproteinase (MMP), tissue inhibitor of metalloproteinase(TIMP), alpha-globulin, actin-severing protein, S100 protein,fibrinopeptide, calcitonin gene-related peptide (CGRP), tachykinin,ghrelin, neurotensin, corticotropin-releasing hormone, and combinationsthereof in said sample; and (b) determining the presence or severity ofIBS in said individual using an algorithm based upon said diagnosticmarker profile.
 34. The method of claim 33, wherein said methodcomprises determining said diagnostic marker profile in combination witha symptom profile, wherein said symptom profile is determined byidentifying the presence or severity of at least one symptom in saidindividual; and determining the presence or severity of IBS in saidindividual using an algorithm based upon said diagnostic marker profileand said symptom profile.
 35. The method of claim 33, wherein saidalgorithm comprises a statistical algorithm.
 36. The method of claim 35,wherein said statistical algorithm comprises a single learningstatistical classifier system or a combination of at least two learningstatistical classifier systems.
 37. The method of claim 33, wherein saidmethod further comprises comparing the presence or severity of IBSdetermined in step (b) to the presence or severity of IBS in saidindividual at an earlier time.
 38. A method for monitoring drug efficacyin an individual receiving a drug useful for treating irritable bowelsyndrome (IBS), said method comprising: (a) determining a diagnosticmarker profile by detecting the presence or level of at least onediagnostic marker selected from the group consisting of a cytokine,growth factor, anti-neutrophil antibody, anti-Saccharomyces cerevisiaeantibody (ASCA), antimicrobial antibody, lactoferrin, anti-tissuetransglutaminase (tTG) antibody, lipocalin, matrix metalloproteinase(MMP), tissue inhibitor of metalloproteinase (TIMP), alpha-globulin,actin-severing protein, S100 protein, fibrinopeptide, calcitoningene-related peptide (CGRP), tachykinin, ghrelin, neurotensin,corticotropin-releasing hormone, and combinations thereof in saidsample; and (b) determining the effectiveness of said drug using analgorithm based upon said diagnostic marker profile.
 39. The method ofclaim 38, wherein said method comprises determining said diagnosticmarker profile in combination with a symptom profile, wherein saidsymptom profile is determined by identifying the presence or severity ofat least one symptom in said individual; and determining theeffectiveness of said drug using an algorithm based upon said diagnosticmarker profile and said symptom profile.
 40. The method of claim 38,wherein said algorithm comprises a statistical algorithm.
 41. The methodof claim 40, wherein said statistical algorithm comprises a singlelearning statistical classifier system or a combination of at least twolearning statistical classifier systems.
 42. The method of claim 38,wherein said method further comprises comparing the effectiveness ofsaid drug determined in step (b) to the effectiveness of said drug insaid individual at an earlier time in therapy.
 43. The method of claim38, wherein said drug is selected from the group consisting of aserotonergic agent, antidepressant, chloride channel activator,guanylate cyclase agonist, antibiotic, opioid, neurokinin antagonist,antispasmodic or anticholinergic agent, belladonna alkaloid,barbiturate, free bases thereof, pharmaceutically acceptable saltsthereof, derivatives thereof, analogs thereof, and combinations thereof.44. A computer-readable medium comprising code for controlling one ormore processors to classify whether a sample from an individual isassociated with irritable bowel syndrome (IBS), said code comprising:instructions to apply a statistical process to a data set comprising adiagnostic marker profile to produce a statistically derived decisionclassifying said sample as an IBS sample or non-IBS sample based uponsaid diagnostic marker profile, wherein said diagnostic marker profileindicates the presence or level of at least one diagnostic markerselected from the group consisting of a cytokine, growth factor,anti-neutrophil antibody, anti-Saccharomyces cerevisiae antibody (ASCA),antimicrobial antibody, lactoferrin, anti-tissue transglutaminase (tTG)antibody, lipocalin, matrix metalloproteinase (MMP), tissue inhibitor ofmetalloproteinase (TIMP), alpha-globulin, actin-severing protein, S100protein, fibrinopeptide, calcitonin gene-related peptide (CGRP),tachykinin, ghrelin, neurotensin, corticotropin-releasing hormone, andcombinations thereof in said sample.
 45. The computer-readable medium ofclaim 44, wherein said computer-readable medium comprises instructionsto apply a statistical process to a data set comprising said diagnosticmarker profile in combination with a symptom profile which indicates thepresence or severity of at least one symptom in said individual toproduce a statistically derived decision classifying said sample as anIBS sample or non-IBS sample based upon said diagnostic marker profileand said symptom profile.
 46. The computer-readable medium of claim 44,wherein said statistical process comprises a single learning statisticalclassifier system.
 47. The computer-readable medium of claim 44, whereinsaid statistical process comprises a combination of at least twolearning statistical classifier systems.
 48. A system for classifyingwhether a sample from an individual is associated with irritable bowelsyndrome (IBS), said system comprising: (a) a data acquisition moduleconfigured to produce a data set comprising a diagnostic marker profile,wherein said diagnostic marker profile indicates the presence or levelof at least one diagnostic marker selected from the group consisting ofa cytokine, growth factor, anti-neutrophil antibody, anti-Saccharomycescerevisiae antibody (ASCA), antimicrobial antibody, lactoferrin,anti-tissue transglutaminase (tTG) antibody, lipocalin, matrixmetalloproteinase (MMP), tissue inhibitor of metalloproteinase (TIMP),alpha-globulin, actin-severing protein, S100 protein, fibrinopeptide,calcitonin gene-related peptide (CGRP), tachykinin, ghrelin,neurotensin, corticotropin-releasing hormone, and combinations thereofin said sample; (b) a data processing module configured to process thedata set by applying a statistical process to the data set to produce astatistically derived decision classifying said sample as an IBS sampleor non-IBS sample based upon said diagnostic marker profile; and (c) adisplay module configured to display the statistically derived decision.49. The system of claim 48, wherein said system comprises a dataacquisition module configured to produce a data set comprising saiddiagnostic marker profile in combination with a symptom profile whichindicates the presence or severity of at least one symptom in saidindividual; a data processing module configured to process the data setby applying a statistical process to the data set to produce astatistically derived decision classifying said sample as an IBS sampleor non-IBS sample based upon said diagnostic marker profile and saidsymptom profile; and a display module configured to display thestatistically derived decision.
 50. The system of claim 48, wherein saidstatistical process comprises a single learning statistical classifiersystem.
 51. The system of claim 48, wherein said statistical processcomprises a combination of at least two learning statistical classifiersystems.